Single-cell RNA-seq data analysis of project pbmc

#     css: "`r params$path_to_git`/css/style.css"

# No encoding supplied: defaulting to UTF-8 ?

# R Options
options(stringsAsFactors=FALSE,
        citation_format="pandoc", 
        dplyr.summarise.inform=FALSE, 
        knitr.table.format="html",
        kableExtra_view_html=TRUE,
        future.globals.maxSize=2000000000, mc.cores=4, 
        future.fork.enable=TRUE, future.plan="multicore",
        future.rng.onMisuse="ignore")

# Python3 needed for clustering, umap, other python packages
# Path to binary will be automatically found
# Set manually if it does not work
reticulate_python3_path = unname(Sys.which("python3"))
Sys.setenv(RETICULATE_PYTHON=reticulate_python3_path)

# Required libraries
library(Seurat) # main
library(ggplot2) # plots
library(patchwork) # combination of plots
library(magrittr) # %>% operator
library(reticulate) # required for "leiden" clustering
library(enrichR) # functional enrichment
library(future) # multicore support for Seurat

# Other libraries we use
# Knit: knitr
# Data handling: dplyr, tidyr, purrr, stringr, Matrix, sctransform, glmGamPoi (optional for speed but only available for R 4.0)
# Tables: kableExtra, DT
# Plots: ggsci
# IO: openxlsx, readr, R.utils
# Annotation: biomaRt
# DEG: mast, limma (for a more efficient implementation of the Wilcoxon Rank Sum Test according to Seurat)
# Functional enrichment: enrichR
# Other: sessioninfo, cerebroApp, sceasy, ROpenSci/bibtex, knitcitations

# Knitr default options
knitr::opts_chunk$set(echo=TRUE,                     # output code
                      cache=FALSE,                   # do not cache results
                      message=TRUE,                  # show messages
                      warning=TRUE,                  # show warnings
                      tidy=FALSE,                    # do not auto-tidy-up code
                      fig.width=10,                  # default fig width in inches
                      class.source="fold-hide",      # by default collapse code blocks
                      dev=c("png", "pdf"),           # create figures in png and pdf; the first device (png) will be used for HTML output
                      dev.args=list(png=list(type="cairo"),  # png: use cairo - works on cluster, supports anti-aliasing (more smooth)
                                    pdf=list(bg="white")),     # pdf: use cairo - works on cluster, supports anti-aliasing (more smooth)
                      dpi=96,                        # figure resolution
                      fig.retina=2                   # retina multiplier
)

# If the DOI publication servers cannot be reached, there will be no citations, knitcitations will not write a references.bib file and pandoc will stop. This makes sure that there is always at least one citation.
invisible(knitcitations::citep(citation("knitr")))

cat(paste0('<link rel="stylesheet" href="', params$path_to_git, '/css/style.css">'))

# Copy input parameters to internal parameter list
param = params
if (is.null(param$samples_to_drop )) param$samples_to_drop = c()
if (is.null(param$vars_to_regress)) param$vars_to_regress = c()
if (is.null(param$latent_vars)) param$latent_vars = c()

# Git directory and files to source must be done first, then all helper functions can be sourced
git_files_to_source = c("functions_io.R",
                        "functions_plotting.R",
                        "functions_analysis.R",
                        "functions_degs.R",
                        "functions_util.R")
git_files_to_source = file.path(param$path_to_git, "R", git_files_to_source)
file_exists = purrr::map_lgl(git_files_to_source, file.exists)
if (any(!file_exists)) stop(paste("The following files could not be found:", paste(git_files_to_source[!file_exists], collapse=", "), ". Please check the git directory at '", param$path_to_git, "'.!"))
invisible(purrr::map(git_files_to_source, source))

# Debugging mode: 
switch (param$debugging_mode, 
        default_debugging=on_error_default_debugging(), 
        terminal_debugger=on_error_start_terminal_debugger(),
        print_traceback=on_error_just_print_traceback(),
        on_error_default_debugging())

# Set output hooks
knitr::knit_hooks$set(message=format_message, warning=format_warning)

# Create output directories
if (!file.exists(param$path_out)) dir.create(param$path_out, recursive=TRUE, showWarnings=FALSE)
dir.create(file.path(param$path_out, "figures"), recursive=TRUE, showWarnings=FALSE)
dir.create(file.path(param$path_out, "marker_degs"), recursive=TRUE, showWarnings=FALSE)
dir.create(file.path(param$path_out, "annotation"), recursive=TRUE, showWarnings=FALSE)
dir.create(file.path(param$path_out, "data"), recursive=TRUE, showWarnings=FALSE)
dir.create(file.path(param$path_out, "export"), recursive=TRUE, showWarnings=FALSE)

# Path for figures in png and pdf format (trailing "/" is needed)
knitr::opts_chunk$set(fig.path=paste0(file.path(param$path_out, "figures"), "/"))

knitr::opts_chunk$set(fig.path=paste(params$path_out, "figures/", sep="/"))

# Path for annotation
param$file_annot = file.path(param$path_out, "annotation", paste0(param$mart_dataset, ".v", param$annot_version, ".annot.txt"))

# Path for cell cycle genes file
param$file_cc_genes = file.path(param$path_out, "annotation", "cell_cycle_markers.xlsx")

# Do checks
error_messages = c()

# Check parameters and parse entries (e.g. numbers) so that they are valid
param = check_parameters_scrnaseq(param)
error_messages = c(error_messages, param[["error_messages"]])
# Check installed packages
error_messages = c(error_messages, check_installed_packages_scrnaseq())
# Check python
error_messages = c(error_messages, check_python())
# Check pandoc
error_messages = c(error_messages, check_pandoc())
# Check enrichR
error_messages = c(error_messages, check_enrichr(param$enrichr_dbs, param$enrichr_site))
# Check ensembl
error_messages = c(error_messages, check_ensembl(biomart="ensembl", 
                                                 dataset=param$mart_dataset, 
                                                 mirror=param$biomart_mirror, 
                                                 version=param$annot_version,
                                                 attributes=param$mart_attributes,
                                                 file_annot=param$file_annot,
                                                 file_cc_markers=param$file_cc_genes))

# Stop here if there are errors
if (length(error_messages)) stop(paste(c("", paste("*", error_messages)), collapse="\n"))

Read data

The workflow can be run for pre-processed 10x data, as well as for pre-processed SmartSeq-2 or other data that are represented by a simple table with transcript counts per gene and cell.

Read and print mapping statistics

Prior to this workflow, raw sequencing reads were mapped to the reference transcriptome. The resulting mapping statistics are printed below for a first estimation of sample quality.

Pre-processing of 10x data with Cell Ranger

We use the 10x Genomics Cell Ranger software to map 10x sequencing reads. The result is a count matrix that contains the number of unique transcripts per gene (rows) and cell (columns). To save storage space, the matrix is converted into a condensed format and described by the following 3 files:

• “features.tsv.gz”: Tabular file with gene annotation (Ensembl gene ID and the gene symbol) to identify genes
• “barcodes.tsv.gz”: File with cell barcodes to identify cells
• “matrix.mtx.gz”: A condensed version of the count matrix

Pre-processing of SmartSeq-2 data

Sequencing reads from SmartSeq-2 (and other) experiments can be mapped with any suitable mapping software as long as a count matrix is generated that contains the number of mapped reads per gene (rows) and cell (columns). The first row of the count matrix should contain cell barcodes or names, the first column of the matrix should contain Ensembl gene IDs.

# Are statistics provided?
if (!all(is.na(param$path_data$stats))) { 
  
  # Loop through all samples and read mapping stats
  mapping_stats_list = list()
  for (i in 1:nrow(param$path_data)) {  
    if (!is.na(param$path_data$stats[i])) { 
      mapping_stats_list[[param$path_data$name[i]]] = read.delim(param$path_data$stats[i], 
                                                                 sep=",", header=FALSE, check.names=FALSE) %>%
        t() %>% as.data.frame()
    } 
  }
  
  # Join all mapping stats tables
  mapping_stats = mapping_stats_list %>% purrr::reduce(dplyr::full_join, by="V1")
  rownames(mapping_stats) = mapping_stats[["V1"]]
  mapping_stats = mapping_stats %>% dplyr::select(-V1)
  colnames(mapping_stats) = names(mapping_stats_list)
 
  # Print table to HTML 
  knitr::kable(mapping_stats, align="l", caption="Mapping statistics") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))
 
} else { 
  message("Mapping statistics cannot be shown. No valid file provided.")
}

× (Message)
Mapping statistics cannot be shown. No valid file provided.

Read gene annotation

We read gene annotation from Ensembl and write the resulting table to file. We generate several dictionaries to translate between Ensembl IDs, gene symbols, Entrez Ids, and Seurat gene names.

# Read annotation from csv or from Ensembl and a tab separated txt will be created
if (file.exists(param$file_annot)) {
  annot_ensembl = read.delim(param$file_annot)
} else {
  annot_mart = suppressWarnings(GetBiomaRt(biomart="ensembl", 
                                           dataset=param$mart_dataset, 
                                           mirror=param$biomart_mirror, 
                                           version=param$annot_version))
  annot_ensembl = biomaRt::getBM(mart=annot_mart, attributes=param$mart_attributes, useCache=FALSE)
  write.table(annot_ensembl, file=param$file_annot, sep="\t", col.names=TRUE, row.names=FALSE, append=FALSE)
  message("Gene annotation file was created at: ", param$file_annot)
  # Note: depending on the attributes, there might be more than one row per gene
}

# Double-check if we got all required annotation, in case annotation file was read
check_annot_main = all(param$annot_main %in% colnames(annot_ensembl))
if (!check_annot_main) {
  stop("The annotation table misses at least one of the following columns: ", paste(param$annot_main, collapse=", "))
}

# Create translation tables
ensembl = param$annot_main["ensembl"]
symbol = param$annot_main["symbol"]
entrez = param$annot_main["entrez"]

# Ensembl id to gene symbol (new)
ensembl_to_symbol = unique(annot_ensembl[, c(ensembl, symbol)])
ensembl_to_symbol[, symbol] = ifelse(nchar(ensembl_to_symbol[, symbol]) == 0, NA, ensembl_to_symbol[, symbol])
ensembl_to_symbol = setNames(ensembl_to_symbol[, symbol], ensembl_to_symbol[, ensembl])

# Ensembl id to seurat-compatible unique rowname (new)
ensembl_to_seurat_rowname = unique(annot_ensembl[, c(ensembl, symbol)])
ensembl_to_seurat_rowname[, symbol] = ifelse(nchar(ensembl_to_seurat_rowname[, symbol]) == 0, ensembl_to_seurat_rowname[, ensembl], ensembl_to_seurat_rowname[, symbol])
ensembl_to_seurat_rowname[, symbol] = make.unique(gsub(pattern="_", replacement="-", x=ensembl_to_seurat_rowname[, symbol], fixed=TRUE))
ensembl_to_seurat_rowname = setNames(ensembl_to_seurat_rowname[, symbol], ensembl_to_seurat_rowname[, ensembl])

# Seurat-compatible unique rowname to ensembl id
seurat_rowname_to_ensembl = setNames(names(ensembl_to_seurat_rowname), ensembl_to_seurat_rowname)

# Ensembl to Entrez
ensembl_to_entrez = unique(annot_ensembl[, c(ensembl, entrez)])
ensembl_to_entrez[, entrez] = ifelse(nchar(ensembl_to_entrez[, entrez]) == 0, NA, ensembl_to_entrez[, entrez])
ensembl_to_entrez = split(ensembl_to_entrez[, entrez], ensembl_to_entrez[, ensembl])

# Seurat-compatible unique rowname to Entrez
seurat_rowname_to_ensembl_match = match(seurat_rowname_to_ensembl, names(ensembl_to_entrez))
names(seurat_rowname_to_ensembl_match) = names(seurat_rowname_to_ensembl)
seurat_rowname_to_entrez = purrr::map(seurat_rowname_to_ensembl_match, function(i) {unname(ensembl_to_entrez[[i]])})

# Entrez IDs is duplicating Ensembl IDs in annot_ensembl
# Therefore, we remove Entrez IDs from the annotation table, after generating all required translation tables
# Set rownames of annotation table to Ensembl identifiers
annot_ensembl = annot_ensembl[, -match(entrez, colnames(annot_ensembl))] %>% unique() %>% as.data.frame()
rownames(annot_ensembl) = annot_ensembl[, ensembl]

# Use biomart to translate human cell cycle genes to the species of interest and save them in a file
if (file.exists(param$file_cc_genes)) {
  # Load from file
  genes_s = openxlsx::read.xlsx(param$file_cc_genes, sheet=1)
  genes_g2m = openxlsx::read.xlsx(param$file_cc_genes, sheet=2)
  
} else { 
  # Obtain from Ensembl
  # Note: both mart objects must point to the same mirror for biomarT::getLDS to work
  mart_human = suppressWarnings(GetBiomaRt(biomart="ensembl", 
                                           dataset="hsapiens_gene_ensembl", 
                                           mirror=param$biomart_mirror, 
                                           version=param$annot_version))
  mart_myspecies = suppressWarnings(GetBiomaRt(biomart="ensembl", 
                                               dataset=param$mart_dataset, 
                                               mirror=GetBiomaRtMirror(mart_human), 
                                               version=param$annot_version)) 
  
  # S phase marker
  genes_s = biomaRt::getLDS(attributes=c("ensembl_gene_id", "external_gene_name"), 
                          filters="external_gene_name", 
                          values=Seurat::cc.genes.updated.2019$s.genes, 
                          mart=mart_human, 
                          attributesL=c("ensembl_gene_id", "external_gene_name"), 
                          martL=mart_myspecies, 
                          uniqueRows=TRUE)
  colnames(genes_s) = c("Human_ensembl_id", "Human_gene_name", "Species_ensembl_id", "Species_gene_name")
  
  # G2/M marker
  genes_g2m = biomaRt::getLDS(attributes=c("ensembl_gene_id", "external_gene_name"), 
                            filters="external_gene_name", 
                            values=Seurat::cc.genes.updated.2019$g2m.genes, 
                            mart=mart_human, 
                            attributesL=c("ensembl_gene_id", "external_gene_name"), 
                            martL=mart_myspecies, 
                            uniqueRows=TRUE)
  colnames(genes_g2m) = c("Human_ensembl_id", "Human_gene_name", "Species_ensembl_id", "Species_gene_name")
  
  # Write to file
  openxlsx::write.xlsx(list(S_phase=genes_s,G2M_phase=genes_g2m), file=param$file_cc_genes)
}

# Convert Ensembl ID to Seurat-compatible unique rowname
genes_s = data.frame(Human_gene_name=genes_s$Human_gene_name, Species_gene_name=unname(ensembl_to_seurat_rowname[genes_s$Species_ensembl_id]))
genes_g2m = data.frame(Human_gene_name=genes_g2m$Human_gene_name, Species_gene_name=unname(ensembl_to_seurat_rowname[genes_g2m$Species_ensembl_id]))

Read scRNA-seq data

We next read the single-cell RNA-seq data into a Seurat object.

What is Seurat and which information is contained in a Seurat object?

Seurat is an R-package that is used for the analysis of single-cell RNA-seq data. We read pre-processed data as described above, and convert it to a count matrix in R. In the case of 10x data, the count matrix contains the number of unique RNA molecules (UMIs) per gene and cell. In the case of SmartSeq-2 data, the count matrix contains the number of reads per gene and cell.

In addition to the count matrix, the workflow stores additional information in the Seurat object, including but not limited to the normalized data, dimensionality reduction and cluster results.

# List of Seurat objects
sc = list()

datasets = param$path_data
for (i in seq(nrow(datasets))) {
  name = datasets[i, "name"]
  type = datasets[i, "type"]
  path = datasets[i, "path"]
  suffix = datasets[i, "suffix"]
  
  # Read 10X or smartseq2
  if (type == "10x") {
    # Read 10X sparse matrix into a Seurat object
    sc = c(sc, ReadSparseMatrix(path, project=name, row_name_column=1, convert_row_names=ensembl_to_seurat_rowname, cellnames_suffix=suffix))
    
  } else if (type == "smartseq2") {
    # Read counts table into a Seurat object
    sc = c(sc, ReadCountsTable(path, project=name, row_name_column=1, convert_row_names=ensembl_to_seurat_rowname, parse_plate_information=TRUE, return_samples_as_datasets=TRUE, cellnames_suffix=suffix))
  } 
}

# Make sure that sample names are unique. If not, just prefix with the dataset name. Also set orig.ident to this name.
sample_names = names(sc)
duplicated_sample_names_idx = which(sample_names %in% sample_names[duplicated(sample_names)])
for (i in duplicated_sample_names_idx) {
  sample_names[i] = paste(head(sc[[i]][["orig.dataset", drop=TRUE]], 1), sample_names[i], sep=".")
  sc[[i]][["orig.ident"]] = sample_names[i]
}

# Set up colors for samples and add them to the sc objects
sample_names = purrr::flatten_chr(purrr::map(sc, function(s) {
  nms = unique(as.character(s[[]][["orig.ident"]]))
  return(nms) 
}))
param$col_samples = GenerateColours(num_colours=length(sample_names), names=sample_names, palette=param$col_palette_samples, alphas=1)
sc = purrr::map(sc, ScAddLists, lists=list(orig.ident=param$col_samples), lists_slot="colour_lists")

# Downsample cells if requested
if (!is.null(param$downsample_cells_n)) {
  sc = purrr::map(sc, function(s) {
    cells = ScSampleCells(sc=s, n=param$downsample_cells_n, seed=1)
    return(subset(s, cells=cells))
  })
}

sc

## $pbmc_10x
## An object of class Seurat 
## 33694 features across 100 samples within 1 assay 
## Active assay: RNA (33694 features, 0 variable features)
## 
## $pbmc_smartseq2_sample1
## An object of class Seurat 
## 33694 features across 100 samples within 1 assay 
## Active assay: RNA (33694 features, 0 variable features)

The following first table shows available metadata (columns) of the first 5 cells (rows). These metadata provide additional information about the cells in the dataset, such as the sample a cell belongs to (“orig.ident”), the number of mapped reads (“nCounts_RNA”), or the above mentioned number of unique genes detected (“nFeature_RNA”). The second table shows available metadata (columns) of the first 5 genes (rows).

# Combine cell metadata of the Seurat objects into one big metadata
sc_cell_metadata = suppressWarnings(purrr::map_dfr(sc, function(s) return(s[[]])) %>% as.data.frame())

# Print cell metadata
knitr::kable(head(sc_cell_metadata), align="l", caption="Cell metadata, top 5 rows") %>%
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))  %>% 
  kableExtra::scroll_box(width="100%")

Cell metadata, top 5 rows

	orig.ident	nCount_RNA	nFeature_RNA	orig.dataset	SampleName	PlateNumber	PlateRow	PlateCol
PBMC1_10x_CACAGATGTAACAGTA-1	pbmc_10x	3538	1316	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_TTGACCCGTTCAGTAC-1	pbmc_10x	7333	2109	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_AGTGCCGCAGGGAATC-1	pbmc_10x	5560	1915	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_GCATCTCTCCTGCTAC-1	pbmc_10x	8918	2801	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_CAAAGAAGTGACGTCC-1	pbmc_10x	4487	1426	pbmc_10x	NA	NA	NA	NA
PBMC1_10x_CGGTCAGCACGGGTAA-1	pbmc_10x	4467	1513	pbmc_10x	NA	NA	NA	NA

# Print gene metadata
knitr::kable(head(sc[[1]][[param$assay_raw]][[]], 5), align="l", caption="Feature metadata, top 5 rows (only first dataset shown)") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))  %>% 
  kableExtra::scroll_box(width="100%")

Feature metadata, top 5 rows (only first dataset shown)

	feature_id	feature_name	feature_type
TSPAN6	ENSG00000000003	TSPAN6	Gene Expression
TNMD	ENSG00000000005	TNMD	Gene Expression
DPM1	ENSG00000000419	DPM1	Gene Expression
SCYL3	ENSG00000000457	SCYL3	Gene Expression
C1orf112	ENSG00000000460	C1orf112	Gene Expression

Pre-processing

The steps below represent a standard pre-processing workflow for single-cell RNA-seq data, including quality control, the respective filtering of cells and genes, data normalization and scaling, and the detection of highly variable genes.

Why is pre-processing so important?

Cells may have been damaged during sample preparation and might be only partially captured in the sequencing. In addition, free-floating mRNA from damaged cells can be encapsulated, adding to the background noise. The low-quality cells and free-floating mRNA interfere with downstream analyses. Therefore, cells and genes are filtered based on defined quality metrics. Data normalization eliminates cell-specific biases such as the absolute number of reads per cell, allowing us to systematically compare cells afterwards. Subsequent scaling corrects for the fact that genes have different lengths, allowing us to compare genes afterwards. Lastly, highly variable genes are determined, reducing computational overhead and noise from genes that are not interesting.

Quality control

We start the analysis by removing unwanted cells from the dataset(s). Three commonly used QC metrics include the number of unique genes detected in each cell (“nFeature_RNA”), the total number of molecules detected in each cell (“nCount_RNA”), and the percentage of counts that map to the mitochrondrial genome (“percent_mt”). If ERCC spike-in controls were used, the percentage of counts mapping to them is also shown (“percent_ercc”).

Which cells can be considered to be of low quality?

On the one hand, low-quality cells such as dying, degraded and damaged cells, and empty droplets will often have very few genes (“nFeature_RNA”) and low total counts (“nCount_RNA”). On the other hand, cell doublets may show an aberrantly high number of genes. Since the total number of reads detected within a cell typically strongly correlates with the number of unique genes, we can focus on the number of unique genes for filtering. In addition, damaged cells often exhibit high mitochondrial (“percent_mt”) or spike-in (“percent_ercc”) content. As mitochondria have their own membranes, their RNA is often the last to degrade in damaged cells and can thus be found in high numbers. However, it is important to keep in mind that different cell types and cells isolated from various species may differ in their number of expressed genes and metabolism. For example, stem cells may express a higher number of unique genes, and metabolically active cells may express a higher number of mitochondrial genes.

Impact of low-quality cells on downstream analyses

First of all, low-quality cells of different cell types might cluster together due to similarities in their damage-induced gene expression profiles. This leads to artificial intermediate states or trajectories between subpopulations that would otherwise be distinct. Second, low-quality cells might mask relevant gene expression changes. The main differences captured by the first principal components will likely be based on cell quality rather than the underlying biology, reducing the power of dimensionality reduction. Likewise, highly variable genes might just be genes that differ between low- and high-quality cells. Lastly and equally important, the low number of total counts in low-quality cells might lead to artificial upregulation of genes due to wrong scaling effects during the normalisation process.

# If filters were specified globally (i.e. not by sample), this chunk will copy them for each sample such that downstream filtering can work by sample
param$cell_filter = purrr::map(list_names(sc), function(s) {
  if (s %in% names(param$cell_filter)) {
    return(param$cell_filter[[s]])
  } else {
    return(param$cell_filter)
  }
})

param$feature_filter = purrr::map(list_names(sc), function(s) {
  if (s %in% names(param$feature_filter)) {
    return(param$feature_filter[[s]])
  } else {
    return(param$feature_filter)
  }
})

# Calculate percentage of counts in mitochondrial genes for each Seurat object
sc = purrr::map(sc, function(s) {
  mt_features = grep(pattern=param$mt, rownames(s), value=TRUE)
  return(Seurat::PercentageFeatureSet(s, features=mt_features, col.name="percent_mt", assay=param$assay_raw))
})

# Calculate percentage of counts in ribosomal genes for each Seurat object
sc = purrr::map(sc, function(s) {
  ribo_features = grep(pattern="^RP[SL]", rownames(s), value=TRUE, ignore.case=TRUE)
  return(Seurat::PercentageFeatureSet(s, features=ribo_features, col.name="percent_ribo", assay=param$assay_raw))
})

# Calculate percentage of counts in ERCC for each Seurat object (if assay is available)
sc = purrr::map(sc, function(s) {
  if ("ERCC" %in% Seurat::Assays(s)) s$percent_ercc = s$nCount_ERCC/(s$nCount_ERCC + s$nCount_RNA)*100
  return(s)
  })

# Combine (again) cell metadata of the Seurat objects into one big metadata, this time including mt and ercc 
sc_cell_metadata = suppressWarnings(purrr::map_dfr(sc, function(s){ return(s[[]]) }) %>% as.data.frame())

# Only RNA assay at the moment
# counts_median uses sapply on the counts matrix, which converts the sparse matrix into a normal matrix
#   This might have to be adapted in future (Sparse Matrix Apply Function)
sc = purrr::map(list_names(sc), function(n) {
  # Calculate percentage of counts per gene in a cell
  counts_rna = Seurat::GetAssayData(sc[[n]], slot="counts", assay=param$assay_raw)
  total_counts = sc[[n]][[paste0("nCount_", param$assay_raw), drop=TRUE]]
  counts_rna_perc = Matrix::t(Matrix::t(counts_rna)/total_counts)*100

  # Calculate feature filters
  num_cells_expr = Matrix::rowSums(counts_rna >= 1)
  num_cells_expr_threshold = Matrix::rowSums(counts_rna >= param$feature_filter[[n]][["min_counts"]])
  
  # Calculate median of counts_rna_perc per gene 
  counts_median = apply(counts_rna_perc, 1, median)
  
  # Add all QC measures as metadata
  sc[[n]][[param$assay_raw]] = Seurat::AddMetaData(sc[[n]][[param$assay_raw]], data.frame(num_cells_expr, num_cells_expr_threshold, counts_median))
  return(sc[[n]])
})

# QC metrics to plot for cells
cell_qc_features = c(paste0(c("nFeature_", "nCount_"), param$assay_raw), "percent_mt")
if ("percent_ercc" %in% colnames(sc_cell_metadata)) cell_qc_features = c(cell_qc_features, "percent_ercc")
cell_qc_features = values_to_names(cell_qc_features)

# Get filter thresholds per QC metrics
cell_qc_thresholds = purrr::map(cell_qc_features, function(m) {
  tresh = purrr::map_dfr(names(param$cell_filter), function(n) {
    if (m %in% names(param$cell_filter[[n]])) {
      t = data.frame(orig.ident=n, min=param$cell_filter[[n]][[m]][1], max=param$cell_filter[[n]][[m]][2]) %>% 
        tidyr::pivot_longer(cols=2:3, names_to=c("threshold")) %>%
        dplyr::filter(!is.na(value))
      t$threshold = factor(t$threshold, levels=c("min", "max"))
      return(t)
    }
  })
})

# Now create plot per QC metric
p_list = list()
for (m in cell_qc_features) {
  p_list[[m]]= ggplot(sc_cell_metadata[, c("orig.ident", m)], aes_string(x="orig.ident", y=m, fill="orig.ident", group="orig.ident")) +
    geom_violin(scale="width")

  # Adds points for samples with less than three cells since geom_violin does not work here
  p_list[[m]] = p_list[[m]] + 
    geom_point(data=sc_cell_metadata[, c("orig.ident", m)] %>% dplyr::filter(orig.ident %in% names(which(table(sc_cell_metadata$orig.ident) < 3))), aes_string(x="orig.ident", y=m, fill="orig.ident"), shape=21, size=2)
  
  # Now add style
  p_list[[m]] = p_list[[m]] + 
    AddStyle(title=m, legend_position="none", fill=param$col_samples, xlab="") + 
    theme(axis.text.x=element_text(angle=45, hjust=1))
  
  # Add filter threshold as segments to plot; min threshold lines are dashed and max threshold lines are twodashed
  if (nrow(cell_qc_thresholds[[m]]) > 0) {
    p_list[[m]] = p_list[[m]] + geom_segment(data=cell_qc_thresholds[[m]], 
                                             aes(x=as.integer(as.factor(orig.ident))-0.5, 
                                                 xend=as.integer(as.factor(orig.ident))+0.5, 
                                                 y=value, yend=value, lty=threshold), colour="firebrick") +
      scale_linetype_manual(values=setNames(c("dashed", "F1"), c("max", "min")))
  }
}
p = patchwork::wrap_plots(p_list, ncol=2) + patchwork::plot_annotation("Distribution of feature values") 
p

# Correlate QC metrics for cells
p_list = list()
sc_cell_metadata_plot_order = sample(1:nrow(sc_cell_metadata))

# nFeature vs nCount
m = paste0(c("nCount_", "nFeature_"), param$assay_raw)
p_list[[1]] = ggplot(sc_cell_metadata[sc_cell_metadata_plot_order, , drop=FALSE], aes_string(x=m[1], y=m[2], colour="orig.ident")) +
  geom_point() + 
  scale_linetype_manual(values=setNames(c("dashed", "F1"), c("max", "min"))) +
  AddStyle(col=param$col_samples)
if (nrow(cell_qc_thresholds[[m[1]]]) > 0) {
  p_list[[1]] = p_list[[1]] + geom_vline(data=cell_qc_thresholds[[m[1]]], aes(xintercept=value, lty=threshold), colour="firebrick", show.legend=FALSE)
    
}
if (nrow(cell_qc_thresholds[[m[2]]]) > 0) {
  p_list[[1]] = p_list[[1]] + geom_hline(data=cell_qc_thresholds[[m[2]]], aes(yintercept=value, lty=threshold), colour="firebrick", show.legend=FALSE)
}
  

# nFeature vs percent_mt
m = c("percent_mt", paste0(c("nFeature_"), param$assay_raw))
p_list[[2]] = ggplot(sc_cell_metadata[sc_cell_metadata_plot_order, , drop=FALSE], aes_string(x=m[1], y=m[2], colour="orig.ident")) +
  geom_point() +
  scale_linetype_manual(values=setNames(c("dashed", "F1"), c("max", "min"))) +
  AddStyle(col=param$col_samples)
if (nrow(cell_qc_thresholds[[m[1]]]) > 0) {
  p_list[[2]] = p_list[[2]] + geom_vline(data=cell_qc_thresholds[[m[1]]], aes(xintercept=value, lty=threshold), colour="firebrick", show.legend=FALSE)
}
if (nrow(cell_qc_thresholds[[m[2]]]) > 0) {
  p_list[[2]] = p_list[[2]] + geom_hline(data=cell_qc_thresholds[[m[2]]], aes(yintercept=value, lty=threshold), colour="firebrick", show.legend=FALSE)
}

# nFeature vs percent_ercc (if available)
if ("percent_ercc" %in% names(cell_qc_features)) {
  m = c("percent_ercc", paste0(c("nFeature_"), param$assay_raw))
  p_list[[3]] = ggplot(sc_cell_metadata[sc_cell_metadata_plot_order, , drop=FALSE], aes_string(x=m[1], y=m[2], colour="orig.ident")) +
    geom_point() + 
    scale_linetype_manual(values=setNames(c("dashed", "F1"), c("max", "min"))) + 
    AddStyle(col=param$col_samples)
  if (nrow(cell_qc_thresholds[[m[1]]]) > 0) {
    p_list[[3]] = p_list[[3]] + geom_vline(data=cell_qc_thresholds[[m[1]]], aes(xintercept=value, lty=threshold), colour="firebrick", show.legend=FALSE)
  }
  if (nrow(cell_qc_thresholds[[m[2]]]) > 0) {
    p_list[[3]] = p_list[[3]] + geom_hline(data=cell_qc_thresholds[[m[2]]], aes(yintercept=value, lty=threshold), colour="firebrick", show.legend=FALSE)
  }
}

# Combine plots
p = patchwork::wrap_plots(p_list, ncol=length(p_list)) + patchwork::plot_annotation("Features plotted against each other")
if (length(p_list) == 1) {
  p = p & theme(legend.position="bottom")
} else {
  p = p + patchwork::plot_layout(guides="collect") & theme(legend.position="bottom")
}
p

Genes with highest expression

We next investigate whether there are individual genes that are represented by an unusually high number of counts. For each cell, we first calculate the percentage of counts per gene. Subsequently, for each gene, we calculate the median value of these percentages in all cells. Genes with the highest median percentage of counts are plotted below.

# Plot only samples that we intend to keep 
sc_names = names(sc)[!(names(sc) %in% param$samples_to_drop)]
genes_highestExpr = lapply(sc_names, function(i) {
  top_ten_exp = sc[[i]][[param$assay_raw]][["counts_median"]] %>% dplyr::arrange(dplyr::desc(counts_median)) %>% head(n=10)
  return(rownames(top_ten_exp))
  }) %>%
  unlist() %>%
  unique()

genes_highestExpr_counts = purrr::map_dfc(sc[sc_names], .f=function(s) s[[param$assay_raw]][["counts_median"]][genes_highestExpr, ]) 
genes_highestExpr_counts$gene = genes_highestExpr
genes_highestExpr_counts = genes_highestExpr_counts %>% tidyr::pivot_longer(cols=all_of(sc_names))
genes_highestExpr_counts$name = factor(genes_highestExpr_counts$name, levels=sc_names)

col =  GenerateColours(num_colours=length(genes_highestExpr), names=genes_highestExpr, palette="ggsci::pal_simpsons")
p = ggplot(genes_highestExpr_counts, aes(x=name, y=value, col=gene, group=gene)) + 
  geom_point() + 
  AddStyle(title="Top 10 highest expressed genes per sample, added into one list", 
           xlab="Sample", ylab="Median % of raw counts\n per gene in a cell", 
           legend_position="bottom", 
           col=col)
if (length(unique(genes_highestExpr_counts$name))>1) p = p + geom_line()
p

Filtering

Cells and genes are filtered based on the following thresholds:

cell_filter_lst = param$cell_filter %>% unlist(recursive=FALSE)
is_numeric_filter = purrr::map_lgl(cell_filter_lst, function(f) return(is.numeric(f) & length(f)==2))

# numeric cell filters
if (length(cell_filter_lst[is_numeric_filter]) > 0) {
  purrr::invoke(rbind, cell_filter_lst[is_numeric_filter]) %>%
    knitr::kable(align="l", caption="Numeric filters applied to cells", col.names=c("Min", "Max")) %>% 
    kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))
}

Numeric filters applied to cells

	Min	Max
pbmc_10x.nFeature_RNA	NA	NA
pbmc_10x.percent_mt	NA	NA
pbmc_smartseq2_sample1.nFeature_RNA	NA	NA
pbmc_smartseq2_sample1.percent_mt	NA	NA

# categorial cell filters
if (length(cell_filter_lst[!is_numeric_filter]) > 0) {
purrr::invoke(rbind, cell_filter_lst[!is_numeric_filter] %>% purrr::map(paste, collapse=",")) %>%
  knitr::kable(align="l", caption="Categorial filters applied to cells", col.names=c("Values")) %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))
}
  
# gene filters
feature_filter_lst = param$feature_filter %>% unlist(recursive=FALSE)
if (length(feature_filter_lst) > 0) {
  purrr::invoke(rbind, feature_filter_lst) %>% 
    knitr::kable(align="l", caption="Filters applied to genes", col.names=c("Value")) %>%
    kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))
}

Filters applied to genes

	Value
pbmc_10x.min_counts	1
pbmc_10x.min_cells	3
pbmc_smartseq2_sample1.min_counts	1
pbmc_smartseq2_sample1.min_cells	3

The number of excluded cells and features is as follows:

# Iterate over datasets and filters
# Record a cell if it does not pass the filter
# Also record a cell if it belongs to a sample that should be dropped
sc_cells_to_exclude  = purrr::map(list_names(sc), function(n) { 
  filter_result = purrr::map(list_names(param$cell_filter[[n]]), function(f) {
    filter = param$cell_filter[[n]][[f]]
    if (is.numeric(filter)) {
      if (is.na(filter[1])) filter[1] = -Inf # Minimum
      if (is.na(filter[2])) filter[2] = Inf  # Maximum 
      idx_exclude = sc[[n]][[f, drop=TRUE]] < filter[1] | sc[[n]][[f, drop=TRUE]] > filter[2]
      return(names(which(idx_exclude)))
    } else if (is.character(filter)) { 
      idx_exclude = !sc[[n]][[f, drop=TRUE]] %in% filter
      return(Cells(sc[[n]])[idx_exclude])
    }
  })

  # Samples to drop
  if (n %in% param$samples_to_drop) {
    filter_result[["samples_to_drop"]] = colnames(sc[[n]])
  } else {
    filter_result[["samples_to_drop"]] = as.character(c())
  }
  
  # Minimum number of cells for a sample to keep
  if (ncol(sc[[n]]) < param$samples_min_cells) {
    filter_result[["samples_min_cells"]] = colnames(sc[[n]])
  } else {
    filter_result[["samples_min_cells"]] = as.character(c())
  }
  
  return(filter_result)
})

# Summarise
sc_cells_to_exclude_summary = purrr::map_dfr(sc_cells_to_exclude, function(s) {
  return(as.data.frame(purrr::map(s, length))) 
  })
rownames(sc_cells_to_exclude_summary) = names(sc_cells_to_exclude)

sc_cells_to_exclude_summary$Original = purrr::map_int(sc, ncol)
sc_cells_to_exclude_summary$Excluded = purrr::map_int(sc_cells_to_exclude, function(s) { return(purrr::flatten(s) %>% unique() %>% length())})
sc_cells_to_exclude_summary$PercKept = round((sc_cells_to_exclude_summary$Original - sc_cells_to_exclude_summary$Excluded) / sc_cells_to_exclude_summary$Original * 100, 2)
knitr::kable(sc_cells_to_exclude_summary, 
             align="l", 
             caption="Summary of excluded cells") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))

Summary of excluded cells

	nFeature_RNA	percent_mt	samples_to_drop	samples_min_cells	Original	Excluded	PercKept
pbmc_10x	0	0	0	0	100	0	100
pbmc_smartseq2_sample1	0	0	0	0	100	0	100

# Add filter column to sc_cell_metadata for post-filtering QC
sc_cell_metadata$IS_FILTERED = rownames(sc_cell_metadata) %in% unlist(sc_cells_to_exclude)

# Now filter, drop the respective colours and adjust integration method
sc = purrr::map(list_names(sc), function(n) {
  cells_to_keep = Cells(sc[[n]])
  cells_to_keep = cells_to_keep[!cells_to_keep %in% purrr::flatten_chr(sc_cells_to_exclude[[n]])]
  if (length(cells_to_keep)==0) return(NULL)
  else return(subset(sc[[n]], cells=cells_to_keep))
}) %>% purrr::discard(is.null)

if (length(sc)==1) param$integrate_samples[["method"]] = "single"

# Only RNA assay at the moment

# Iterate over samples and record a feature if it does not pass the filter
sc_features_to_exclude = purrr::map(list_names(sc), function(n) {
  
  # Make sure the sample contains more cells than the minimum threshold
  if (length(Cells(sc[[n]])) < param$feature_filter[[n]][["min_cells"]]) return(list())
  
  # Return gene names that do not pass the minimum threshold 
  else return(names(which(sc[[n]][[param$assay_raw]][["num_cells_expr_threshold", drop=TRUE]] < param$feature_filter[[n]][["min_cells"]])))
})

# Which genes are to be filtered for all samples?
# Note: Make sure that no other sample is called "AllSamples"
sc_features_to_exclude$AllSamples = Reduce(f=intersect, x=sc_features_to_exclude)

# Summarise
sc_features_to_exclude_summary = purrr::map_dfr(names(sc), function(n){
  df = data.frame(Original=nrow(sc[[n]]), 
                  FailThreshold=length(sc_features_to_exclude[[n]]))
  df$PercFailThreshold = round(df$FailThreshold / df$Original * 100, 2)
  df$Kept = length(setdiff(rownames(sc[[n]]), sc_features_to_exclude[["AllSamples"]]))
  df$PercKept = round(df$Kept / df$Original * 100, 2)
  return(df)
})
rownames(sc_features_to_exclude_summary) = names(sc)
knitr::kable(sc_features_to_exclude_summary, align="l", caption="Summary of excluded genes") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))

Summary of excluded genes

	Original	FailThreshold	PercFailThreshold	Kept	PercKept
pbmc_10x	33694	23929	71.02	14388	42.7
pbmc_smartseq2_sample1	33694	19906	59.08	14388	42.7

# Now filter those genes that are to be filtered for all samples 
sc = purrr::map(list_names(sc), function(n) {
  assay_names = Seurat::Assays(sc[[n]])
  features_to_keep = purrr::map(values_to_names(assay_names), function(a) {
    features = rownames(sc[[n]][[a]])
    keep = features[!features %in% sc_features_to_exclude$AllSamples]
    return(keep)
  })
  return(subset(sc[[n]], features=purrr::flatten_chr(features_to_keep)))
})

After filtering, the size of the Seurat object is:

sc

## $pbmc_10x
## An object of class Seurat 
## 14388 features across 100 samples within 1 assay 
## Active assay: RNA (14388 features, 0 variable features)
## 
## $pbmc_smartseq2_sample1
## An object of class Seurat 
## 14388 features across 100 samples within 1 assay 
## Active assay: RNA (14388 features, 0 variable features)

Quality control post filtering

The updated QC plots are:

# Now create plot per QC metric after filtering
p_list = list()
for (m in cell_qc_features) {
  p_list[[m]]= ggplot(sc_cell_metadata[, c("orig.ident", m, "IS_FILTERED")] %>% dplyr::filter(!IS_FILTERED), aes_string(x="orig.ident", y=m, fill="orig.ident", group="orig.ident")) +
    geom_violin(scale="width")

  # Adds points for samples with less than three cells since geom_violin does not work here
  p_list[[m]] = p_list[[m]] + 
    geom_point(data=sc_cell_metadata[, c("orig.ident", m, "IS_FILTERED")] %>% dplyr::filter(!IS_FILTERED, orig.ident %in% names(which(table(sc_cell_metadata$orig.ident) < 3))), aes_string(x="orig.ident", y=m, fill="orig.ident"), shape=21, size=2)
  
  # Now add style
  p_list[[m]] = p_list[[m]] + 
    AddStyle(title=m, legend_position="none", fill=param$col_samples, xlab="") + 
    theme(axis.text.x=element_text(angle=45, hjust=1))
  
  # Add filter threshold as segments to plot; min threshold lines are dashed and max threshold lines are twodashed
  if (nrow(cell_qc_thresholds[[m]]) > 0) {
    p_list[[m]] = p_list[[m]] + geom_segment(data=cell_qc_thresholds[[m]], 
                                             aes(x=as.integer(as.factor(orig.ident))-0.5, 
                                                 xend=as.integer(as.factor(orig.ident))+0.5, 
                                                 y=value, yend=value, lty=threshold), colour="firebrick") +
      scale_linetype_manual(values=setNames(c("dashed", "F1"), c("max", "min")))
  }
}
p = patchwork::wrap_plots(p_list, ncol=2) + patchwork::plot_annotation("Distribution of feature values") 
p

# Correlate QC metrics for cells
p_list = list()
sc_cell_metadata_plot_order = sample(1:nrow(sc_cell_metadata))

# nFeature vs nCount
m = paste0(c("nCount_", "nFeature_"), param$assay_raw)
p_list[[1]] = ggplot(sc_cell_metadata[sc_cell_metadata_plot_order, , drop=FALSE], aes_string(x=m[1], y=m[2], colour="orig.ident", alpha="IS_FILTERED")) +
  geom_point() + 
  scale_linetype_manual(values=setNames(c("dashed", "F1"), c("max", "min"))) +
  scale_alpha_manual(values=setNames(c(1, 0.1), c(FALSE, TRUE))) +
  AddStyle(col=param$col_samples)
if (nrow(cell_qc_thresholds[[m[1]]]) > 0) {
  p_list[[1]] = p_list[[1]] + geom_vline(data=cell_qc_thresholds[[m[1]]], aes(xintercept=value, lty=threshold), colour="firebrick", show.legend=FALSE)
}
if (nrow(cell_qc_thresholds[[m[2]]]) > 0) {
  p_list[[1]] = p_list[[1]] + geom_hline(data=cell_qc_thresholds[[m[2]]], aes(yintercept=value, lty=threshold), colour="firebrick", show.legend=FALSE)
}
  

# nFeature vs percent_mt
m = c("percent_mt", paste0(c("nFeature_"), param$assay_raw))
p_list[[2]] = ggplot(sc_cell_metadata[sc_cell_metadata_plot_order, , drop=FALSE], aes_string(x=m[1], y=m[2], colour="orig.ident", alpha="IS_FILTERED")) +
  geom_point() +
  scale_linetype_manual(values=setNames(c("dashed", "F1"), c("max", "min"))) +
  scale_alpha_manual(values=setNames(c(1, 0.1), c(FALSE, TRUE))) +
  AddStyle(col=param$col_samples)
if (nrow(cell_qc_thresholds[[m[1]]]) > 0) {
  p_list[[2]] = p_list[[2]] + geom_vline(data=cell_qc_thresholds[[m[1]]], aes(xintercept=value, lty=threshold), colour="firebrick", show.legend=FALSE)
}
if (nrow(cell_qc_thresholds[[m[2]]]) > 0) {
  p_list[[2]] = p_list[[2]] + geom_hline(data=cell_qc_thresholds[[m[2]]], aes(yintercept=value, lty=threshold), colour="firebrick", show.legend=FALSE)
}

# nFeature vs percent_ercc
if ("percent_ercc" %in% names(cell_qc_features)) {
  m = c("percent_ercc", paste0(c("nFeature_"), param$assay_raw))
  p_list[[3]] = ggplot(sc_cell_metadata[sc_cell_metadata_plot_order, , drop=FALSE], aes_string(x=m[1], y=m[2], colour="orig.ident", alpha="IS_FILTERED")) +
    geom_point() + 
    scale_linetype_manual(values=setNames(c("dashed", "F1"), c("max", "min"))) +
    scale_alpha_manual(values=setNames(c(1, 0.1), c(FALSE, TRUE)), show.legend=FALSE) +
    AddStyle(col=param$col_samples)
  if (nrow(cell_qc_thresholds[[m[1]]]) > 0) {
    p_list[[3]] = p_list[[3]] + geom_vline(data=cell_qc_thresholds[[m[1]]], aes(xintercept=value, lty=threshold), colour="firebrick", show.legend=FALSE)
  }
  if (nrow(cell_qc_thresholds[[m[2]]]) > 0) {
    p_list[[3]] = p_list[[3]] + geom_hline(data=cell_qc_thresholds[[m[2]]], aes(yintercept=value, lty=threshold), colour="firebrick", show.legend=FALSE)
  }
}

# Combine plots
p = patchwork::wrap_plots(p_list, ncol=length(p_list)) + patchwork::plot_annotation("Features plotted against each other")
if (length(p_list)==1) {
  p = p & theme(legend.position="bottom")
} else {
  p = p + patchwork::plot_layout(guides="collect") & theme(legend.position="bottom")
}
p

Normalisation

In this section, we subsequently run a series of Seurat functions for each provided sample:

1. We start by running a standard log normalisation, where counts for each cell are divided by the total counts for that cell and multiplied by 10,000. This is then natural-log transformed.

What do we need normalisation for?

The number of raw sequencing reads per cell is influenced by technical differences in the capture, reverse transcription and sequencing of RNA molecules, particularly due to the difficulty of achieving consistent library preparation with minimal starting material. Thus, comparing gene expression between cells may reveal differences that are solely due to sampling effects. After low-quality cells were removed in the previous step, the primary goal of normalization is to remove technical sampling effects while preserving the true biological signal.

Count depth scaling is the simplest and most commonly used normalization strategy. The underlying idea is that each cell initially contained an equal number of mRNA molecules, and differences arise due to sampling effects. For each cell, the number of reads per gene is divided by a cell-specific “size factor,” which is proportional to the total count depth of the cell. The resulting normalized data add up to 1 per cell, and is then typically multiplied by a factor of 10 (10,000 in this workflow).

Finally, normalized data are log-transformed for three important reasons. First, distances between log-transformed expression data represent log fold changes. Log-transformation emphasizes contributions from genes with strong relative differences, for example a gene that is expressed at an average count of 50 in cell type A and 10 in cell type B rather than a gene that is expressed at an average count of 1100 in A and 1000 in B. Second, log-transformation mitigates the relation between mean and variance in the data. Lastly, log-transformation reduces that skewness of the data as many downstream analyses require the data to be normally distributed.

2. We assign cell cycle scores to each cell based on its normalised expression of G2/M and S phase markers. These scores are visualised in a separate section further below. If specified in the above parameter section, cell cycle effects are removed during scaling (step 3). Cell cycle effects removed for this report: FALSE; all cell cycle effects removed for this report: FALSE.

How does removal of cell cycle effects affect the data?

Note that removing all signal associated to cell cycle can negatively impact downstream analysis. For example, in differentiating processes, stem cells are quiescent and differentiated cells are proliferating (or vice versa), and removing all cell cycle effects can blur the distinction between these cells. An alternative approach is to remove the difference between G2M and S phase scores. This way, signals separating non-cycling and cycling cells will be maintained, while differences amongst proliferating cells will be removed. For a more detailed explanation, see the cell cycle vignette for Seurat https://satijalab.org/seurat/v3.1/cell_cycle_vignette.html.

3. Dependent on the normalisation of your choice, we either

a. Run standard functions to select variable genes, and scale normalised gene counts. For downstream analysis it is beneficial to focus on genes that exhibit high cell-to-cell variation, that is they are highly expressed in some cells and lowly in others. To be able to compare normalised gene counts between genes, gene counts are further scaled to have zero mean and unit variance (z-score).

What do we need scaling for?

After normalization, gene expression data can be compared between cells. However, expression of individual genes still cannot be compared. This is because genes have different lengths and, depending on the experimental set up, longer genes can be represented by a higher number of reads. To account for this effect, normalized data are further scaled using a z-transformation, resulting in the average expression of 0 and the variance of 1 for each gene across all cells. Note that additional unwanted sources of variations can be regressed out during the scaling process, such as cell cycle effects or the percentage of mitochondrial reads.

b. Run SCTransform, a new and more sophisticated normalisation method that replaces the previous functions (normalisation, variable genes and scaling).

What is SCTransform special about?

The standard log-transformation applied in step 1 assumes that count depth influences all genes equally. However, it has been shown that the use of a single size factor will introduce different effects on highly and lowly expressed genes (10.1186/s13059-019-1874-1). SCTransform is a new statistical approach for the modelling, normalization and variance stabilization of single-cell RNA-seq data, and is an alternative to steps 1 and 3a described above. Note that SCTransform has been developed for UMI count data and can therefore safely be applied to 10x but not SmartSeq-2 data. As for the scaling in step 3a, additional unwanted sources of variations can be regressed out during SCTransform.

Normalisation method used for this report: RNA; with additional sources of variance regressed out: .

While raw data is typically used for statistical tests such as finding marker genes, normalised data is mainly used for visualising gene expression values. Scaled data include variable genes only, potentially without cell cycle effects, and are mainly used to determine the structure of the dataset(s) with Principal Component Analysis, and indirectly to cluster and visualise cells in 2D space.

# Normalise data the original way
#   This is required to score cell cycle (https://github.com/satijalab/seurat/issues/1679)
sc = purrr::map(sc, Seurat::NormalizeData, normalization.method="LogNormalize", scale.factor=10000, verbose=FALSE)

# Determine cell cycle effect per sample 
sc = purrr::map(list_names(sc), function(n) {
  sc[[n]] = CCScoring(sc=sc[[n]], genes_s=genes_s[,2], genes_g2m=genes_g2m[,2], name=n)
  if (any(is.na(sc[[n]][["S.Score"]])) | any(is.na(sc[[n]][["G2M.Score"]]))) {
    param$cc_remove=FALSE
    param$cc_remove_all=FALSE
    param$cc_rescore_after_merge=FALSE
  }
  return(sc[[n]])
})

# If cell cycle effects should be removed, we first score cells 
# The effect is then removed in the following chunk 
if (param$cc_remove) {
# Add to vars that need to regressed out during normalisation
  if (param$cc_remove_all) {
    # Remove all signal associated to cell cycle
    param$vars_to_regress = unique(c(param$vars_to_regress, "S.Score", "G2M.Score"))
    param$latent_vars = unique(c(param$latent_vars, "S.Score", "G2M.Score"))
  } else {
    # Don't remove the difference between cycling and non-cycling cells 
    param$vars_to_regress = unique(c(param$vars_to_regress, "CC.Difference"))
    param$latent_vars = unique(c(param$latent_vars, "CC.Difference"))
  }  
}

if (param$norm == "RNA") { 
  # Find variable features from normalised data (unaffected by scaling)
  sc = purrr::map(sc, Seurat::FindVariableFeatures, selection.method="vst", nfeatures=3000, verbose=FALSE)
  
  # Scale 
  # Note: For a single dataset where no integration/merging is needed, all features can already be scaled here. 
  #   Otherwise, scaling of all features will be done after integration/merging.
  if (param$integrate_samples[["method"]]=="single") {
    sc[[1]] = Seurat::ScaleData(sc[[1]], 
                      features=rownames(sc[[1]][["RNA"]]),
                      vars.to.regress=param$vars_to_regress, 
                      verbose=FALSE) 
  }
} else if (param$norm == "SCT") {
  # Run SCTransform
  #
  # This is a new normalisation method that replaces previous Seurat functions "NormalizeData", "FindVariableFeatures", and "ScaleData". 
  # vignette: https://satijalab.org/seurat/v3.0/sctransform_vignette.html
  # paper: https://www.biorxiv.org/content/10.1101/576827v2
  # Normalised data end up here: sc@assays$SCT@data
  # Note: For a single dataset where no integration is needed, all features can already be scaled here. 
  #   Otherwise, it is enough to scale only the variable features.
  # Note: It is not guaranteed that all genes are successfully normalised with SCTransform. 
  #   Consequently, some genes might be missing from the SCT assay. 
  #   See: https://github.com/ChristophH/sctransform/issues/27
  # Note: The performance of SCTransform can be improved by using "glmGamPoi" instead of "poisson" as method for initial parameter estimation.
  sc = purrr::map(list_names(sc), function(n) { 
    SCTransform(sc[[n]], 
                assay=param$assay_raw,
                vars.to.regress=param$vars_to_regress, 
                min_cells=param$feature_filter[[n]][["min_cells"]], 
                verbose=FALSE, 
                return.only.var.genes=!(param$integrate_samples[["method"]]=="single"),
                method=ifelse(packages_installed("glmGamPoi"), "glmGamPoi", "poisson")) 
  })
}

Experience shows that 1,000-2,000 genes with the highest cell-to-cell variation are often sufficient to describe the global structure of a single-cell dataset. For example, cell type-specific genes typically highly vary between cells. Housekeeping genes, on the other hand, are similarly expressed across cells and can be disregarded to differentiate between cells. Highly variable genes are typically the genes with a cell type specific expression profile, and are often the genes of interest in single-cell experiments. Housekeeping genes, with similar levels of expression across all cells, or genes with minor expression differences, might add random noise and mask relevant changes during downstream dimensionality reduction and clustering. We therefore aim to select a sensible set of variable genes that includes interesting biological signal and excludes noise. Here, the top 3,000 variable genes are selected and used for downstream analysis.

How are variable genes selected?

To determine variable genes, we need to separate biological variability from technical variability. Technical variability arises especially for lowly expressed genes, where high variability corresponds to small absolute changes that we are not interested in. Here, we use the variance-stabilizing transformation (vst) method implemented in Seurat (10.1186/s13059-019-1874-1). This method first models the technical variability as a relationship between mean gene expression and variance using local polynomial regression. The model is then used to calculate the expected variance based on the observed mean gene expression. The difference between the observed and expected variance is called residual variance and likely reflects biological variability.

fig_height_vf = 5 * ceiling(length(names(sc))/2)

p_list = purrr::map(list_names(sc), function(n) {
  top10 = head(Seurat::VariableFeatures(sc[[n]], assay=ifelse(param$norm=="SCT", param$norm, param$assay_raw)), 10)
  p = Seurat::VariableFeaturePlot(sc[[n]], 
                                  assay=ifelse(param$norm=="SCT", param$norm, param$assay_raw), 
                                  selection.method=ifelse(param$norm=="RNA", "vst", "sct"), 
                                  col=c("grey", param$col)) + 
    AddStyle(title=n) + 
    theme(legend.position=c(0.2, 0.8), legend.background=element_rect(fill=alpha("white", 0.0)))
  p = LabelPoints(plot=p, points=top10, repel=TRUE, xnudge=0, ynudge=0)
  return(p)
})

p = patchwork::wrap_plots(p_list, ncol=2) + patchwork::plot_annotation("Variable genes")
p

Combining multiple samples

if (param$integrate_samples[["method"]]!="single") {
  
  # When merging, feature meta-data is removed by Seurat entirely; save separately for each assay except for SCT and add again afterwards
  # Note: not sure whether this is still needed - discuss
  assay_names = setdiff(unique(purrr::flatten_chr(purrr::map(list_names(sc), function(n) { Seurat::Assays(sc[[n]]) } ))), "SCT")

  # Loop through all assays and accumulate meta data
  sc_feature_metadata = purrr::map(values_to_names(assay_names), function(a) {
    # "feature_id", "feature_name", "feature_type" are accumulated for all assays and stored just once
    # This step is skipped for assays that do not contain all three types of feature information
    contains_neccessary_columns = purrr::map_lgl(list_names(sc), function(n) { 
      all(c("feature_id", "feature_name", "feature_type") %in% colnames(sc[[n]][[a]][[]])) 
      })

    if (all(contains_neccessary_columns)) {
      feature_id_name_type = purrr::map(sc, function(s) return(s[[a]][[c("feature_id", "feature_name", "feature_type")]]) )
      feature_id_name_type = purrr::reduce(feature_id_name_type, function(df_x, df_y) {
        new_rows = which(!rownames(df_y) %in% rownames(df_x))
        if (length(new_rows) > 0) return(rbind(df_x, df_y[new_rows, ]))
        else return(df_x)
      })
      feature_id_name_type$row_names = rownames(feature_id_name_type)
    } else {
      feature_id_name_type = NULL
    }
    
    # For all other meta-data, we prefix column names with the dataset
    other_feature_data = purrr::map(list_names(sc), function(n) {
      df = sc[[n]][[a]][[]]
      if (contains_neccessary_columns[[n]]) df = df %>% dplyr::select(-dplyr::one_of(c("feature_id", "feature_name", "feature_type"), c()))
      if (ncol(df) > 0) colnames(df) = paste(n, colnames(df), sep=".")
      df$row_names = rownames(df)
      return(df)
    })
    
    # Now join everything by row_names by full outer join
    if (!is.null(feature_id_name_type)) {
      feature_data = purrr::reduce(c(list(feature_id_name_type=feature_id_name_type), other_feature_data), dplyr::full_join, by="row_names")
    } else {
      feature_data = purrr::reduce(other_feature_data, dplyr::full_join, by="row_names")
    }
    rownames(feature_data) = feature_data$row_names
    feature_data$row_names = NULL
    
    return(feature_data)
  })
  
  # When merging, cell meta-data are merged but factors are not kept
  sc_cell_metadata = suppressWarnings(purrr::map_dfr(sc, function(s){ s[[]] }) %>% as.data.frame())
  sc_cell_metadata_factor_levels = purrr::map(which(sapply(sc_cell_metadata, is.factor)), function(n) {
    return(levels(sc_cell_metadata[, n, drop=TRUE]))
  })
}

# Standard method for integrating multiple samples. Best performance but computationally intensive.
if (param$integrate_samples[["method"]]=="integrate") {
  if (!"use_reciprocal_pca" %in% names(param$integrate_samples)) param$integrate_samples[["use_reciprocal_pca"]] = FALSE

  # Run integration
  sc = RunIntegration(sc, 
                      assay=param$norm,
                      ndims=param$integrate_samples[["dimensions"]], 
                      verbose=FALSE, 
                      reference=param$integrate_samples[["reference"]], 
                      use_reciprocal_pca=param$integrate_samples[["use_reciprocal_pca"]], 
                      k_filter=param$integrate_samples[["k.filter"]], 
                      k_weight=param$integrate_samples[["k.weight"]], 
                      k_anchor=param$integrate_samples[["k.anchor"]],
                      k_score=param$integrate_samples[["k.score"]])

  # Re-score cell cycle effects after integration
  if (param$cc_rescore_after_merge) {
    sc = CCScoring(sc=sc, genes_s=genes_s[,2], genes_g2m=genes_g2m[,2])
    if (any(is.na(sc[["S.Score"]])) | any(is.na(sc[["G2M.Score"]])))  {
      param$cc_remove=FALSE
      param$cc_remove_all=FALSE
      param$cc_rescore_after_merge=FALSE
      param$vars_to_regress = setdiff(param$vars_to_regress, c("S.Score", "G2M.Score", "CC.Difference"))
      param$latent_vars = setdiff(param$latent_vars, c("S.Score", "G2M.Score", "CC.Difference"))
    }
  }
  
  # (Re-)Run scaling
  if (param$norm == "RNA") {
    # According to Seurat, we need to scale data again for "RNAintegrated", and "RNA"
    DefaultAssay(sc) = "RNAintegrated"
    sc = Seurat::ScaleData(sc, 
                                features=rownames(sc[["RNAintegrated"]]), 
                                vars.to.regress=param$vars_to_regress, 
                                assay="RNAintegrated",
                                verbose=FALSE)
      
    DefaultAssay(sc) = "RNA"
    sc = Seurat::ScaleData(sc, 
                                features=rownames(sc[["RNA"]]), 
                                vars.to.regress=param$vars_to_regress, 
                                assay="RNA",
                                verbose=FALSE)
  } else if (param$norm == "SCT") {
    # We need to re-run SCTransform for the "SCT" assay again, to normalise on the complete dataset
    DefaultAssay(sc) = "SCT"
    min_cells_overall = max(purrr::map_int(param$feature_filter, function(f) as.integer(f[["min_cells"]])))
    sc = SCTransform(sc, 
                     assay=param$assay_raw, 
                     vars.to.regress=param$vars_to_regress, 
                     min_cells=min_cells_overall,
                     verbose=FALSE,
                     return.only.var.genes=FALSE,
                     method=ifelse(packages_installed("glmGamPoi"), "glmGamPoi", "poisson"))
  }
  
  # Add feature metadata
  for (a in Seurat::Assays(sc)) {
    if (a %in% names(sc_feature_metadata)) {
      sc[[a]] = Seurat::AddMetaData(sc[[a]], sc_feature_metadata[[a]][rownames(sc[[a]]),, drop=FALSE])
    }
  }
  
  # Fix cell metadata factors
  for (f in names(sc_cell_metadata_factor_levels)) {
    sc[[f]] = factor(sc[[f, drop=TRUE]], levels=sc_cell_metadata_factor_levels[[f]])
  }
  
  # Add sample colours again to misc slot
  sc = ScAddLists(sc, lists=list(orig.ident=param$col_samples), lists_slot="colour_lists")

  # Set default assay (will be the integrated version)
  DefaultAssay(sc) = paste0(param$norm, "integrated")  
  
  message("Data values for all samples have been integrated.")
  print(sc)
}

× (Message)
Data values for all samples have been integrated.

## An object of class Seurat 
## 17388 features across 200 samples within 2 assays 
## Active assay: RNAintegrated (3000 features, 3000 variable features)
##  1 other assay present: RNA

Relative log expression

n_cells_rle_plot = 100

# Sample at most 100 cells per dataset and save their identity
cells_subset = sc[["orig.ident"]] %>% tibble::rownames_to_column() %>% 
  dplyr::group_by(orig.ident) %>% 
  dplyr::sample_n(size=min(n_cells_rle_plot, length(orig.ident))) %>% 
  dplyr::select(rowname, orig.ident)

To better understand the efficiency of the applied normalisation procedures, we plot the relative log expression of genes in at most 100 randomly selected cells per sample before and after normalisation. This type of plot reveals unwanted variation in your data. The concept is taken from 10.1371/journal.pone.0191629. In brief, we remove variation between genes, leaving only variation between samples. If expression levels of most genes are similar in all cell types, sample heterogeneity is a sign of unwanted variation.

For each gene, we calculate its median expression across all cells, and then calculate the deviation from this median for each cell. For each cell, we plot the median expression (black), the interquartile range (lightgrey), whiskers defined as 1.5 times the interquartile range (darkgrey), and outliers (#374E55FF, #DF8F44FF)

Raw counts

# Plot raw data
p = PlotRLE(as.matrix(log2(GetAssayData(subset(sc, cells=cells_subset$rowname), assay=param$assay_raw, slot="counts") + 1)), 
            id=cells_subset$orig.ident, 
            col=param$col_samples) + 
  labs(title="log2(raw counts + 1)")
p

Normalised data

Dependent on the context, this tab refers to different data:

• Single sample: RNA normalisation of the single sample
  
• Multiple samples that were merged: Combined RNA normalisation post merging of all samples
  
• Multiple samples that were integrated: Separate RNA normalisation prior to integration of all samples

# Plot normalised data
p = PlotRLE(as.matrix(GetAssayData(subset(sc, cells=cells_subset$rowname), assay=ifelse(param$norm=="SCT", param$norm, param$assay_raw), slot="data")), 
            id=cells_subset$orig.ident, 
            col=param$col_samples) + 
  labs(title="Normalised data")
p

Integrated data

if (! param$integrate_samples[["method"]] %in% c("single", "merge")) {
  # Plot integrated data
  p = PlotRLE(as.matrix(GetAssayData(subset(sc, cells=cells_subset$rowname), assay=paste0(param$norm, "integrated"), slot="data")), 
              id=cells_subset$orig.ident, 
              col=param$col_samples) + 
    labs(title="Integrated data")
  p
} else {
  message("No integrated data available.")
}

Dimensionality reduction

A single-cell dataset of 20,000 genes and 5,000 cells has 20,000 dimensions. At this point of the analysis, we have already reduced the dimensionality of the dataset to 3,000 variable genes. The biological manifold however can be described by far fewer dimensions than the number of (variable) genes, since expression profiles of different genes are correlated if they are involved in the same biological process. Dimension reduction methods aim to find these dimensions. There are two general purposes for dimension reduction methods: to summarize a dataset, and to visualize a dataset.

We use Principal Component Analysis (PCA) to summarize a dataset, overcoming noise and reducing the data to its essential components. Later, we use Uniform Manifold Approximation and Projection (UMAP) to visualize the dataset, placing similar cells together in 2D space, see below.

PCA in a nutshell

Principal Component Analysis is a way to summarize a dataset and to reduce noise by averaging similar gene expression profiles. The information for correlated genes is compressed into single dimensions called principal components (PCs) and the analysis identifies those dimensions that capture the largest amount of variation. This process gives a more precise representation of the patterns inherent to complex and large datasets.

In a PCA, the first PC captures the greatest variance across the whole dataset. The next PC captures the greatest remaining amount of variance, and so on. This way, the top PCs are likely to represent the biological signal where multiple genes are affected by the same biological processes in a coordinated way. In contrast, random technical or biological noise that affects each gene independently are contained in later PCs. Downstream analyses can be restricted to the top PCs to focus on the most relevant biological signal and to reduce noise and unnecessary computational overhead.

To decide how many PCs to include in downstream analyses, we visualise the cells and genes that define the PCA.

# Run PCA for default normalisation
sc = Seurat::RunPCA(sc, features=Seurat::VariableFeatures(object=sc), verbose=FALSE, npcs=min(50, ncol(sc)))

p_list = Seurat::VizDimLoadings(sc, dims=1:2, reduction="pca", col=param$col, combine=FALSE, balanced=TRUE)
for (i in seq(p_list)) p_list[[i]] = p_list[[i]] + AddStyle()
p =  patchwork::wrap_plots(p_list, ncol=2) + patchwork::plot_annotation("Top gene loadings of the first two PCs") 
p

p = Seurat::DimPlot(sc, reduction="pca", cols=param$col_samples) + 
  AddStyle(title="Cells arranged by the first two PCs", legend_position="bottom")
p

p_list = Seurat::DimHeatmap(sc, dims=1:min(20, ncol(sc)), cells=min(500, ncol(sc)), balanced=TRUE, fast=FALSE, combine=FALSE)
p_list = purrr::map(seq(p_list), function(i) {
  p_list[[i]] = p_list[[i]] + 
    ggtitle(paste("PC", i)) + 
    theme(legend.position="none", axis.text.y=element_text(size=8))
  return(p_list[[i]])
  })
p = patchwork::wrap_plots(p_list, ncol=3) + patchwork::plot_annotation("Top gene loadings of the first PCs")
p

Dimensionality of the dataset

We next need to decide how many PCs we want to use for our analyses. PCs include biological signal as well as noise, and we need to determine the number of PCs with which we include as much biological signal as possible and as little noise as possible. The following “Elbow plot” is designed to help us make an informed decision. It shows PCs ranked based on the percentage of variance they explain.

How do we determine the number of PCs for downstream analysis?

The top PC captures the greatest variance across cells and each subsequent PC represents decreasing levels of variance. By visual inspection of the Elbow plot, we try to find the point at which we can explain most of the variance across cells. Commonly, the top 10 PCs are chosen. It may be helpful to repeat downstream analyses with a different number of PCs, although the results often do not differ dramatically. Note that it is recommended to rather choose too many than too few PCs.

# More approximate technique used to reduce computation time
p = Seurat::ElbowPlot(sc, ndims=min(20, ncol(sc))) + 
  geom_vline(xintercept=param$pc_n + .5, col="firebrick", lty=2) + 
  AddStyle(title="Elbow plot") 
p

# Cannot have more PCs than number of cells
param$pc_n = min(param$pc_n, ncol(sc))

For the current dataset, 10 PCs were chosen.

Clustering

Seurat’s clustering method first constructs a graph structure, where nodes are cells and edges are drawn between cells with similar gene expression patterns. Technically speaking, Seurat first constructs a K-nearest neighbor (KNN) graph based on Euclidean distance in PCA space, and refines edge weights between cells based on the shared overlap in their local neighborhoods (Jaccard similarity). To partition the graph into highly interconnected parts, cells are iteratively grouped together using the Leiden algorithm 10.1038/s41598-019-41695-z.

Further explanation on clustering

At this point, we would like to define subpopulations of cells with similar gene expression profiles using unsupervised clustering. Clusters ultimately serve as approximations for biological objects like cell types or cell states.

During the first step of clustering, a K-nearest neighbor (KNN) graph is constructed. In simplified terms this means that cells are connected to their K nearest neighbors based on cell-to-cell expression similarity using the PCs chosen in the previous step. The higher the similarity is, the higher the edge weight becomes. During the second step, the graph is partitioned into highly interconnected communities, whereby each community represents a cluster of cells with similar expression profiles. The separation of the graph into clusters is dependent on the chosen resolution. For scRNA-seq datasets of around 3000 cells, it is recommended to use a resolution value between 0.4 and 1.2. This value can be set even higher for larger datasets. Note that the choice of PCs and cluster resolution is an arbitrary one. Therefore, it is highly recommended to evaluate clusters and re-run the workflow with adapted parameters if needed.

To get a first idea about how different cluster resolution values influence the clustering, we run and visualize the clustering multiple times, see below. For this report, you chose the resolution value 0.5 as the final value for further analyses.

cluster_resolutions = sort(unique(c(param$cluster_resolution, param$cluster_resolution_test)))

# Construct phylogenetic tree relating the "average" cell from each sample
if (length(levels(sc$orig.ident)) > 1) {
  sc = suppressWarnings(Seurat::BuildClusterTree(sc, features=rownames(sc), verbose=FALSE))
  Seurat::Misc(sc, "trees") = list(orig.ident = Seurat::Tool(sc, "Seurat::BuildClusterTree"))
}

# The number of clusters can be optimized by tuning "resolution" -> based on feedback from the client whether or not clusters make sense
# Choose the number of PCs to use for clustering
sc = Seurat::FindNeighbors(sc, dims=1:param$pc_n, verbose=FALSE)

# Seurat vignette suggests resolution parameter between 0.4-1.2 for datasets of about 3k cells
# But we can run multiple resolutions if requested
sc = Seurat::FindClusters(sc, resolution=cluster_resolutions, algorithm=4, verbose=FALSE, method="igraph")

# Construct phylogenetic tree relating the "average" cell from each cluster for each clustering
# Also add colour lists for each clustering
for(r in cluster_resolutions) {
  n = paste0(DefaultAssay(sc), "_snn_res.", r)
  
  # Tree
  if (length(levels(sc[[n, drop=TRUE]])) > 1) {
    Seurat::Idents(sc) = n
    sc = suppressWarnings(Seurat::BuildClusterTree(sc, dims=1:param$pc_n, verbose=FALSE))
    l = list(Seurat::Tool(sc, "Seurat::BuildClusterTree"))
    names(l) = n
    suppressWarnings({Seurat::Misc(sc, "trees") = c(Seurat::Misc(sc, "trees"), l)})
  }
  
  col = GenerateColours(num_colours=length(levels(sc[[n, drop=TRUE]])), names=levels(sc[[n, drop=TRUE]]), 
                                     palette=param$col_palette_clusters, alphas=1)
  # Colours
  l = list(col)
  names(l) = n
  sc = ScAddLists(sc, lists=l, lists_slot="colour_lists")
}

# Set default clustering
n = paste0(DefaultAssay(sc), "_snn_res.", param$cluster_resolution)
sc$seurat_clusters = sc[[n, drop=TRUE]]
Seurat::Idents(sc) = sc$seurat_clusters
if (length(levels(sc$seurat_clusters)) > 1) {
  suppressWarnings({Seurat::Misc(sc, "trees") = c(Seurat::Misc(sc, "trees"), list(seurat_clusters = Seurat::Misc(sc, "trees")[[n]]))})
}

# Set up colors for default clustering
sc = ScAddLists(sc, lists=list(seurat_clusters=Misc(sc, "colour_lists")[[n]]), lists_slot="colour_lists")
param$col_clusters = Misc(sc, "colour_lists")[["seurat_clusters"]]

# Default UMAP
sc = suppressWarnings(Seurat::RunUMAP(sc, dims=1:param$pc_n, verbose=FALSE, umap.method="uwot"))

# If there are any test resolutions other than the default, go ahead
if (length(cluster_resolutions) > 1) {
  p_list = list()
  for(r in cluster_resolutions) {
    r = as.character(r)
    n = paste0(DefaultAssay(sc), "_snn_res.", r)
    
    cluster_cells = table(sc[[n, drop=TRUE]])
    cluster_labels = paste0(names(cluster_cells)," (", cluster_cells,")")
    
    p_list[[r]] = Seurat::DimPlot(sc, reduction="umap", group.by=n, pt.size=param$pt_size) + 
      scale_color_manual(values=Seurat::Misc(sc, "colour_lists")[[n]], labels=cluster_labels) +
      AddStyle(title=r, legend_position="bottom", legend_title="Clusters") +
      FontSize(x.text = 10, y.text = 10, x.title = 13, y.title = 13, main = 15)
    p_list[[r]] = LabelClusters(p_list[[r]], id=n, box=TRUE, fill="white")
  }
  
  p = patchwork::wrap_plots(p_list, ncol=3) + patchwork::plot_annotation(title="UMAP, cells coloured by cluster identity for different resolution values")
  print(p)
}

Visualisation with UMAP

We use a UMAP to visualise and explore a dataset. The goal is to place similar cells together in 2D space, and learn about the biology underlying the data. Cells are color-coded according to the graph-based clustering, and clusters typcially co-localise on the UMAP.

Take care not to mis-read a UMAP:

• Parameters influence the plot (we use defaults here)
• Cluster sizes relative to each other mean nothing, since the method has a local notion of distance
• Distances between clusters might not mean anything
• You may need more than one plot

For a nice read to intuitively understand UMAP, see https://pair-code.github.io/understanding-umap/.

Coloured by cluster

# Note that you can set `label = TRUE` or use the LabelClusters function to help label individual clusters
cluster_cells = table(sc@active.ident)
cluster_labels = paste0(levels(sc@active.ident)," (", cluster_cells[levels(sc@active.ident)],")")
p = Seurat::DimPlot(sc, reduction="umap", group.by="seurat_clusters") + 
  scale_color_manual(values=param$col_clusters, labels=cluster_labels) +
  AddStyle(title="UMAP, cells coloured by cluster identity", legend_position="bottom", legend_title="Clusters")
p = LabelClusters(p, id="seurat_clusters", box=TRUE, fill="white")
p

Coloured by cluster (per sample)

# Plot all samples separately
p = Seurat::DimPlot(sc, reduction="umap", group.by="seurat_clusters", pt.size=param$pt_size, split.by = "orig.ident", ncol = 2) +
  scale_color_manual(values=param$col_clusters, labels=cluster_labels) +
  AddStyle(title="UMAP, cells coloured by cluster identity", legend_position="bottom", legend_title="Clusters")
p = LabelClusters(p, id="seurat_clusters", box=TRUE, fill="white")
p

Coloured by sample

sample_cells = table(sc$orig.ident)
sample_labels = paste0(levels(sc$orig.ident)," (", sample_cells[levels(sc$orig.ident)],")")
# Note: This is a hack to colour by sample but label by Cluster
p = Seurat::DimPlot(sc, reduction="umap", group.by="orig.ident") +
  scale_color_manual(values=param$col_samples, labels=sample_labels) +
  AddStyle(title="UMAP, cells coloured by sample of origin", legend_position="bottom")
p$data$seurat_clusters = sc[["seurat_clusters"]][rownames(p$data), ]
p = LabelClusters(p, id="seurat_clusters", box=TRUE, segment.color="black", fill="white")
p

Coloured by sample (per sample)

# Plot all samples separately
p = Seurat::DimPlot(sc, reduction="umap", group.by="orig.ident", pt.size=param$pt_size, split.by = "orig.ident", ncol = 2) +
  scale_color_manual(values=param$col_samples, labels=sample_labels) +
  AddStyle(title="UMAP, cells coloured by sample of origin", legend_position="bottom")
p$data$seurat_clusters = sc[["seurat_clusters"]][rownames(p$data), ]
p = LabelClusters(p, id="seurat_clusters", box=TRUE, segment.color="black", fill="white")
p

Distribution of cells in clusters

# Count cells per cluster per sample 
cell_samples = sc[[]] %>% dplyr::pull(orig.ident) %>% levels()
cell_clusters = sc[[]] %>% dplyr::pull(seurat_clusters) %>% levels()

tbl = dplyr::count(sc[[c("orig.ident", "seurat_clusters")]], orig.ident, seurat_clusters) %>% tidyr::pivot_wider(names_from="seurat_clusters", names_prefix="Cl_", values_from=n, values_fill=0) %>% as.data.frame()
rownames(tbl) = paste0(tbl[,"orig.ident"],"_n")
tbl[,"orig.ident"] = NULL

# Add percentages
tbl_perc = round(t(tbl) / colSums(tbl) * 100, 2) %>% t()
rownames(tbl_perc) = gsub(rownames(tbl_perc), pattern="_n$", replacement="_perc", perl=TRUE)
tbl = rbind(tbl, tbl_perc)

# Add enrichment
if (length(cell_samples) > 1 & length(cell_clusters) > 1) tbl = rbind(tbl, CellsFisher(sc))

# Sort
tbl = tbl[order(rownames(tbl)),, drop=FALSE]

# Plot percentages
tbl_bar = tbl[paste0(cell_samples, "_perc"), , drop=FALSE] %>% 
  tibble::rownames_to_column(var="Sample") %>%
  tidyr::pivot_longer(tidyr::starts_with("Cl"), names_to="Cluster", values_to="Percentage")
tbl_bar$Cluster = tbl_bar$Cluster %>% gsub(pattern="^Cl_", replacement="", perl=TRUE) %>% factor(levels=sc$seurat_clusters %>% levels())
tbl_bar$Sample = tbl_bar$Sample %>% gsub(pattern="_perc$", replacement="", perl=TRUE) %>% as.factor()
tbl_bar$Percentage = as.numeric(tbl_bar$Percentage)
p = ggplot(tbl_bar, aes(x=Cluster, y=Percentage, fill=Sample)) + 
  geom_bar(stat="identity" ) +
  AddStyle(title="Percentage cells of samples in clusters",
           fill=param$col_samples,
           legend_title="Sample",
           legend_position="bottom")
p

The following table shows the number of cells per sample and cluster:

• n: Number of cells per sample and cluster
  
• perc: Percentage of cells per sample and cluster compared to all other cells of that cluster

In case the dataset contains 2 or more samples, we also calculate whether or not the number of cells of a sample in a cluster is significantly higher or lower than expected:

• oddsRatio: Odds ratio calculated for cluster c1 and sample s1 as (# cells s1 in c1 / # cells not s1 in c1) / (# cells s1 not in c1 / # cells not s1 not in c1)
  
• p: P-value calculated with a Fisher test to test whether “n” is higher or lower than expected

# Print table
knitr::kable(tbl, align="l", caption="Number of cells per sample and cluster") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover")) %>% 
  kableExtra::scroll_box(width="100%")

Number of cells per sample and cluster

	Cl_1	Cl_2	Cl_3
pbmc_10x_n	54	35	11
pbmc_10x_perc	53.47	48.61	40.74
pbmc_10x.oddsRatio	1.32	0.92	0.65
pbmc_10x.p	2.0e-01	6.7e-01	8.9e-01
pbmc_smartseq2_sample1_n	47	37	16
pbmc_smartseq2_sample1_perc	46.53	51.39	59.26
pbmc_smartseq2_sample1.oddsRatio	0.76	1.09	1.54
pbmc_smartseq2_sample1.p	8.7e-01	4.4e-01	2.0e-01

# Reset default assay, so we won't plot integrated data
# Note: We need integrated data for UMAP, clusters
DefaultAssay(sc) = ifelse(param$norm=="SCT", param$norm, param$assay_raw)

Cell Cycle Effect

How much do gene expression profiles in the dataset reflect the cell cycle phases the single cells were in? After initial normalisation, we determined the effects of cell cycle heterogeneity by calculating a score for each cell based on its expression of G2M and S phase markers. Scoring is based on the strategy described in 10.1126/science.aad0501, and human gene symbols are translated to gene symbols of the species of interest using biomaRt. This section of the report visualises the above calculated cell cycle scores.

# Set up colours for cell cycle effect and add to sc object
col =  GenerateColours(num_colours=length(levels(sc$Phase)), names=levels(sc$Phase), palette="ggsci::pal_npg", alphas=1)
sc = ScAddLists(sc, lists=list(Phase=col), lists_slot="colour_lists")

# Get a feeling for how many cells are affected
p1 = ggplot(sc[[]], aes(x=S.Score, y=G2M.Score, colour=Phase)) + 
  geom_point() + 
  scale_x_continuous("G1/S score") + 
  scale_y_continuous("G2/M score") + 
  AddStyle(col=Misc(sc, "colour_lists")[["Phase"]])

p2 = ggplot(sc@meta.data %>% 
              dplyr::group_by(seurat_clusters, Phase) %>% 
              dplyr::summarise(num_cells=length(Phase)), 
            aes(x=seurat_clusters, y=num_cells, fill=Phase)) + 
  geom_bar(stat="identity", position="fill") + 
  scale_x_discrete("Seurat clusters") + 
  scale_y_continuous("Fraction of cells") + 
  AddStyle(fill=Misc(sc, "colour_lists")[["Phase"]])

p3 = ggplot(sc[[]] %>% 
              dplyr::group_by(orig.ident, Phase) %>% 
              dplyr::summarise(num_cells=length(Phase)), 
            aes(x=orig.ident, y=num_cells, fill=Phase)) + 
  geom_bar(stat="identity", position="fill") + 
  scale_y_continuous("Fraction of cells") +
  AddStyle(fill=Misc(sc, "colour_lists")[["Phase"]]) + 
  theme(axis.text.x = element_text(angle=30, hjust=1, vjust=1)) + xlab("")

p = p1 + p2 + p3 & theme(legend.position="bottom")
p = p + patchwork::plot_annotation(title="Cell cycle phases") + plot_layout(guides="collect")
p

UMAP coloured by cell cycle phases

if (any(!is.na(sc$Phase))) {
  # UMAP with phases superimposed
  # Note: This is a hack to colour by phase but label by Cluster
  p = Seurat::DimPlot(sc, group.by="Phase", pt.size=1, cols=Misc(sc, "colour_lists")[["Phase"]]) + 
    AddStyle(title="UMAP, cells coloured by cell cycle phases", legend_title="Phase")
  p$data$seurat_clusters = sc[["seurat_clusters"]][rownames(p$data), ]
  p = LabelClusters(p, id="seurat_clusters", box=TRUE, fill="white")
  p
}

UMAP coloured by S phase

if (any(!is.na(sc$Phase))) {
  p = Seurat::FeaturePlot(sc, features="S.Score", pt.size=1, min.cutoff="q1", max.cutoff="q99", cols=c("lightgrey", param$col)) +
    AddStyle(title="UMAP, cells coloured by S phase")
  p = LabelClusters(p, id="ident", box=TRUE, fill="white")
  p
}

UMAP coloured by G2/M phase

if (any(!is.na(sc$Phase))) {
  p = Seurat::FeaturePlot(sc, features="G2M.Score", pt.size=1, min.cutoff="q1", max.cutoff="q99", cols=c("lightgrey", param$col)) + 
    AddStyle(title="UMAP, cells coloured by G2M phase")
  p = LabelClusters(p, id="ident", box=TRUE, fill="white")
  p
}

UMAP coloured by the difference between S and G2/M phase

if (any(!is.na(sc$Phase))) {
  p = Seurat::FeaturePlot(sc, features="CC.Difference", pt.size=1, min.cutoff="q1", max.cutoff="q99", cols=c("lightgrey", param$col)) +
    AddStyle(title="UMAP, cells coloured by CC.Difference")
  p = LabelClusters(p, id="ident", box=TRUE, fill="white")
  p
}

Cluster QC

Do cells in individual clusters have particularly high counts, detected genes or mitochondrial content?

Number of counts

qc_feature = paste0("nCount_", param$assay_raw)
p1 = suppressMessages(Seurat::FeaturePlot(sc, features=qc_feature) + 
  AddStyle(title="Feature plot") + 
  scale_colour_gradient(low="lightgrey", high=param$col, trans="log10"))
p1 = LabelClusters(p1, id="ident", box=FALSE)


p2 = ggplot(sc[[]], aes_string(x="seurat_clusters", y=qc_feature, fill="seurat_clusters", group="seurat_clusters")) + 
  geom_violin(scale="width") + 
  AddStyle(title="Violin plot (log10 scale)", fill=param$col_clusters,
           xlab="Cluster", legend_position="none") + 
  scale_y_log10()

p = p1 | p2 
p = p + patchwork::plot_annotation(title=paste0("Summed raw counts (", qc_feature, ", log10 scale)"))
p

m = paste0(c("nCount_", "nFeature_"), param$assay_raw)
p = ggplot(sc[[c(m, "seurat_clusters")]], aes_string(x=m[1], y=m[2], colour="seurat_clusters")) + 
  geom_point() + 
  AddStyle(col=param$col_clusters) +
  facet_wrap(~seurat_clusters) +
  theme(legend.position = "none")
p

Number of features

qc_feature = paste0("nFeature_", param$assay_raw)
p1 = suppressMessages(Seurat::FeaturePlot(sc, features=qc_feature) + 
  AddStyle(title="Feature plot") + 
  scale_colour_gradient(low="lightgrey", high=param$col, trans="log10"))
p1 = LabelClusters(p1, id="ident", box=FALSE)

p2 = ggplot(sc[[]], aes_string(x="seurat_clusters", y=qc_feature, fill="seurat_clusters", group="seurat_clusters")) + 
  geom_violin(scale="width") + 
  AddStyle(title="Violin plot", fill=param$col_clusters,
           xlab="Cluster", legend_position="none") + 
  scale_y_log10()

p = p1 | p2 
p = p + patchwork::plot_annotation(title=paste0("Number of features with raw count > 0 (", qc_feature, ", log10 scale)"))
p

m = paste0(c("nFeature_", "nFeature_"), param$assay_raw)
p = ggplot(sc[[c(m, "seurat_clusters")]], aes_string(x=m[1], y=m[2], colour="seurat_clusters")) + 
  geom_point() + 
  AddStyle(col=param$col_clusters) +
  facet_wrap(~seurat_clusters) +
  theme(legend.position = "none")
p

Percent mitochondrial reads

p1 = Seurat::FeaturePlot(sc, features="percent_mt", cols=c("lightgrey", param$col)) + 
  AddStyle(title="Feature plot")
p1 = LabelClusters(p1, id="ident", box=FALSE)

p2 = ggplot(sc[[]], aes(x=seurat_clusters, y=percent_mt, fill=seurat_clusters, group=seurat_clusters)) + 
  geom_violin(scale="width") + 
  AddStyle(title="Violin plot", fill=param$col_clusters,
           xlab="Cluster", legend_position="none")
p = p1 | p2 
p = p + patchwork::plot_annotation(title="Percent of mitochondrial features (percent_mt)")
p

m = c("percent_mt", paste0("nFeature_", param$assay_raw))
p = ggplot(sc[[c(m, "seurat_clusters")]], aes_string(x=m[1], y=m[2], colour="seurat_clusters")) + 
  geom_point() + 
  AddStyle(col=param$col_clusters) +
  facet_wrap(~seurat_clusters) +
  theme(legend.position = "none")
p

Percent ribosomal reads

p1 = Seurat::FeaturePlot(sc, features="percent_ribo", cols=c("lightgrey", param$col)) +
  AddStyle(title="Feature plot")
p1 = LabelClusters(p1, id="ident", box=FALSE)

p2 = ggplot(sc[[]], aes(x=seurat_clusters, y=percent_ribo, fill=seurat_clusters, group=seurat_clusters)) +
  geom_violin(scale="width") +
  AddStyle(title="Violin plot", fill=param$col_clusters,
           xlab="Cluster", legend_position="none")
p = p1 | p2
p = p + patchwork::plot_annotation(title="Percent of ribosomal features (percent_ribo)")
p

m = c("percent_ribo", paste0("nFeature_", param$assay_raw))
p = ggplot(sc[[c(m, "seurat_clusters")]], aes_string(x=m[1], y=m[2], colour="seurat_clusters")) + 
  geom_point() + 
  AddStyle(col=param$col_clusters) +
  facet_wrap(~seurat_clusters) +
  theme(legend.position = "none")
p

Percent ERCC

if ("percent_ercc" %in% colnames(sc[[]])) {
  p1 = Seurat::FeaturePlot(sc, features="percent_ercc", cols=c("lightgrey", param$col)) +
    AddStyle(title="Feature plot")
  p1 = LabelClusters(p1, id="ident", box=FALSE)
  
  p2 = ggplot(sc[[]], aes(x=seurat_clusters, y=percent_ercc, fill=seurat_clusters, group=seurat_clusters)) +
    geom_violin(scale="width") +
    AddStyle(title="Violin plot", fill=param$col_clusters,
             xlab="Cluster", legend_position="none")
  p = p1 | p2
  p = p + patchwork::plot_annotation(title="Percent of ERCC features (percent_ercc)")
  p
  
  # Plot percent_ribo vs nFeature by cluster
  m = c("percent_ercc", paste0("nFeature_", param$assay_raw))
  p = ggplot(sc[[c(m, "seurat_clusters")]], aes_string(x=m[1], y=m[2], colour="seurat_clusters")) + 
    geom_point() + 
    AddStyle(col=param$col_clusters) +
    facet_wrap(~seurat_clusters) +
    theme(legend.position = "none")
  p
} else {
  message("No ERCC controls found.")
}

× (Message)
No ERCC controls found.

Known marker genes

Do cells in individual clusters express provided known marker genes?

known_markers_list=c()

# Overwrite empty list of known markers 
if (!is.null(param$file_known_markers)) {
  # Read known marker genes and map to rownames
  known_markers = openxlsx::read.xlsx(param$file_known_markers)
  known_markers_list = lapply(colnames(known_markers), function(x) {
    y = ensembl_to_seurat_rowname[known_markers[,x]] %>% 
      na.exclude() %>% unique() %>% sort()
    m = !y %in% rownames(sc)
    if (any(m)){
      Warning(paste0("The following genes of marker list '", x, "' cannot be found in the data: ", first_n_elements_to_string(y[m], n=10)))
    }
    return(y[!m])
  })
  
  # Remove empty lists
  names(known_markers_list) = colnames(known_markers)
  is_empty = purrr::map_int(known_markers_list, .f=length) == 0 
  known_markers_list = known_markers_list[!is_empty]
  
  # Add lists to sc object
  sc = ScAddLists(sc, lists=setNames(known_markers_list, paste0("known_marker_", names(known_markers_list))), lists_slot="gene_lists")
}  

# Set plot options
if(length(known_markers_list) > 0) { 
  known_markers_n = length(known_markers_list) 
  known_markers_vect = unlist(known_markers_list) %>% unique() %>% sort()
  idx_dotplot = sapply(seq(known_markers_list), function(x) length(known_markers_list[[x]]) <= 50)
  idx_avgplot = sapply(seq(known_markers_list), function(x) length(known_markers_list[[x]]) >= 10)
} else { 
  known_markers_n=0
  idx_dotplot = idx_avgplot = FALSE
  known_markers_vect = c()
}

# Dotplots and average feature plots
# The height of 1 row (= 1 plot) is fixed to 5 
fig_height_knownMarkers_dotplot = max(5, 5 * sum(idx_dotplot))
fig_height_knownMarkers_avgplot = max(5, 5 * sum(idx_avgplot))

# Individual feature plots
# Each row contains 2 plots
# We fix the height of each plot to the same height as is used later for DEGs
height_per_row = max(2, 0.3 * length(levels(sc$seurat_clusters)))
nr_rows = ceiling(length(known_markers_vect)/2)
fig_height_knownMarkers_vect = max(5, height_per_row * nr_rows)

You provided 6 list(s) of known marker genes. In the following tabs, you find:

• Dot plots for all gene lists containing at most 50 genes
  
• Average feature plots for all gene lists containing at least 10 genes
  
• Individual feature plots for all genes if there are no more than 100 genes in total

Dot plot(s)

A dot plot visualises how gene expression changes across different clusters. The size of a dot encodes the percentage of cells in a cluster that expresses the gene, while the color encodes the scaled average expression across all cells within the cluster. Per gene (column), we group cells based on cluster identity (rows), calculate average expression per cluster, subtract the mean of average expression values and divide by the standard deviation. The resulting scores describe how high or low a gene is expressed in a cluster compared to all other clusters.

if ((known_markers_n > 0) & any(idx_dotplot)) {
  known_markers_dotplot = known_markers_list[idx_dotplot]
  p_list = list()
  for (i in seq(known_markers_dotplot)) {
    g = known_markers_dotplot[[i]]
    g = g[length(g):1]
    p_list[[i]] = suppressMessages(
      Seurat::DotPlot(sc, features=g) + 
        scale_colour_gradient2(low="steelblue", mid="lightgrey", high="darkgoldenrod1") +
        AddStyle(title=paste("Known marker genes:", names(known_markers_dotplot)[i]), ylab="Cluster") + 
        theme(axis.text.x=element_text(angle=90, hjust=1, vjust=.5)) +
        lims(size=c(0,100))
      )
  }
  p = patchwork::wrap_plots(p_list, ncol=1)
  p
} else if ((known_markers_n > 0) & !any(idx_dotplot)) {
  message("This tab is used for dot plots for up to 50 genes. All provided lists are longer than this, and hence dot plots are skipped.")
} else {
  message("No known marker genes were provided and hence dot plots are skipped.")
}

Average feature plot(s)

An average feature plot visualises the average gene expression of each gene list on a single-cell level, subtracted by the aggregated expression of control feature sets. The color of the plot encodes the calculated scores, whereat positive scores suggest that genes are expressed more highly than expected.

if ((known_markers_n > 0) & any(idx_avgplot)) {
  known_markers_avgplot = known_markers_list[idx_avgplot]
  sc = Seurat::AddModuleScore(sc, features=known_markers_avgplot, ctrl=10, name="known_markers")
  idx_replace_names = grep("^known_markers[0-9]+$", colnames(sc@meta.data), perl=TRUE)
  colnames(sc@meta.data)[idx_replace_names] = names(known_markers_avgplot)
  p_list = Seurat::FeaturePlot(sc, features=names(known_markers_avgplot), cols=c("lightgrey", param$col), combine=FALSE, label=TRUE)
  for (i in seq(known_markers_avgplot)) {
    p_list[[i]] = p_list[[i]] + AddStyle(title=paste("Known marker genes:", names(known_markers_avgplot)[i]))
  }
  p = patchwork::wrap_plots(p_list, ncol=1)
  print(p)
} else if ((known_markers_n > 0) & !any(idx_avgplot)) {
  message("This tab is used to plot an average for 10 or more genes. All provided lists are shorter than this, and hence average feature plots are skipped.")
} else {
  message("No known marker genes were provided and hence average feature plots are skipped.")
}

× (Message)
This tab is used to plot an average for 10 or more genes. All provided lists are shorter than this, and hence average feature plots are skipped.

Individual feature plots

An individual feature plot colours single cells on the UMAP according to their normalised gene expression.

if ((known_markers_n > 0) & length(known_markers_vect) <= 100) {
  p_list = Seurat::FeaturePlot(sc, features=known_markers_vect, cols=c("lightgrey", param$col), combine=FALSE, label=TRUE)
  for (i in seq(p_list)) p_list[[i]] = p_list[[i]] + AddStyle()
  p = patchwork::wrap_plots(p_list, ncol=2)
  print(p)
} else if (length(known_markers_vect) > 100) { 
  message("This tab is used to plot up to 100 known marker genes. Your provided list is longer than this, and hence individual feature plots are skipped.")
} else {
  message("No known marker genes were provided and hence individual feature plots are skipped.")
}

Marker genes

We next identify genes that are differentially expressed in one cluster compared to all other clusters, based on raw RNA data and the method “MAST.” Resulting p-values are adjusted using the Bonferroni method. However, note that the p-values are likely inflated, since both clusters and marker genes were determined based on the same gene expression data, and there ought to be gene expression differences by design. Nevertheless, p-values can be used to sort and prioritize marker genes. We require marker genes to be expressed in at least 25% of cells in the respective cluster, with a minimum log2 fold change of 1 and adjusted p-value of at most 0.05. The names of differentially expressed genes per cluster, alongside statistical measures and additional gene annotation are written to file.

What are marker genes?

As described above, cell clusters approximate cell types and states. But how do we know which cell types these are? To characterize cell clusters, we identify marker genes. Good marker genes are genes that are particularly expressed in one cluster, and existing knowledge of these marker genes can be used to extrapolate biological functions for the cluster. A good clustering of cells typically results in good marker genes. Hence, if you cannot find good marker genes you may need to go back to the start of the workflow and adapt your parameters. Note that we also determine genes that are particularly down-regulated in one cluster, even if these are not marker genes in the classical sense.

Good marker genes are highly and possibly even only expressed in one cluster as compared to all other clusters. However, sometimes marker genes are also expressed in other clusters, or are declared as marker genes in these clusters, for example cell lineage markers that are shared by different cell subtypes. To evaluate marker genes, it is essential to visualize their expression patterns.

In addition to detecting marker genes, it might be informative to detect genes that are differentially expressed between one specific cluster and one or several other clusters. This approach allows a more specific distinction of individual clusters and investigation of more subtle differences, see the section “Differentially expressed genes” below.

# Find DEGs for every cluster compared to all remaining cells, report positive (=markers) and negative ones
# min.pct = requires feature to be detected at this minimum percentage in either of the two groups of cells 
# logfc.threshold = requires a feature to be differentially expressed on average by some amount between the two groups
# only.pos = find only positive markers 

# Review recommends using "MAST"; Mathias uses "LR"
# ALWAYS USE: assay="RNA"/"Spatial" or assay="SCT"
# DONT USE: assay=integrated datasets; this data is normalised and contains only 2k genes
# Note: By default, the function uses slot="data". Mast requires log data, so this is the correct way to do it.
#   https://www.bioconductor.org/packages/release/bioc/vignettes/MAST/inst/doc/MAST-interoperability.html
markers = suppressMessages(Seurat::FindAllMarkers(sc, assay=param$assay_raw, test.use="MAST",
                                               only.pos=FALSE, min.pct=param$marker_pct, logfc.threshold=param$marker_log2FC,
                                               latent.vars=param$latent_vars, verbose=FALSE, silent=TRUE))

# If no markers were found, initialise the degs table so that further downstream (export) chunks run
if (ncol(markers)==0) markers = DegsEmptyMarkerResultsTable(levels(sc$seurat_clusters))

# For Seurat versions until 3.2, log fold change is based on the natural log. Convert to log base 2.
if ("avg_logFC" %in% colnames(markers) & !"avg_log2FC" %in% colnames(markers)) {
  lfc_idx = grep("avg_log\\S*FC", colnames(markers))
  markers[,lfc_idx] = marker_deg_results[,lfc_idx] / log(2)
  col_nms = colnames(markers)
  col_nms[2] = "avg_log2FC"
  colnames(markers) = col_nms
}

# Sort markers
markers = markers %>% DegsSort(group=c("cluster"))
  
# Filter markers 
markers_filt = DegsFilter(markers, cut_log2FC=param$marker_log2FC, cut_padj=param$marker_padj)
markers_found = nrow(markers_filt$all)>0

# Add average data to table
markers_out = cbind(markers_filt$all, DegsAvgDataPerIdentity(sc, genes=markers_filt$all$gene, assay=param$assay_raw))

# Split by cluster and write to file
additional_readme = data.frame(Column=c("cluster",
                                        "p_val_adj_score",
                                        "avg_<assay>_<slot>_id<cluster>"), 
                               Description=c("Cluster",
                                             "Score calculated as follows: -log10(p_val_adj)*sign(avg_log2FC)",
                                             "Average expression value for cluster; <assay>: RNA or SCT; <slot>: raw counts or normalised data"))

invisible(DegsWriteToFile(split(markers_out, markers_out$cluster),
                                       annot_ensembl=annot_ensembl,
                                       gene_to_ensembl=seurat_rowname_to_ensembl,
                                       additional_readme=additional_readme,
                                       file=file.path(param$path_out, "marker_degs", "markers_cluster_vs_rest.xlsx")))


# Plot number of differentially expressed genes
p = DegsPlotNumbers(markers_filt$all, 
                      group="cluster", 
                      title=paste0("Number of DEGs, comparing each cluster to the rest\n(FC=", 2^param$marker_log2FC, ", adj. p-value=", param$marker_padj, ")")) 

# Add marker table to seurat object
Seurat::Misc(sc, "markers") = list(condition_column="seurat_clusters", test="MAST", padj=param$marker_padj, 
                                   log2FC=param$marker_log2FC, min_pct=param$marker_pct, assay=param$assay_raw, slot="data",
                                   latent_vars=param$latent_vars,
                                   results=markers_filt$all)

# Add marker lists to seurat object
marker_genesets_up = split(markers_filt$up$gene, markers_filt$up$cluster)
names(marker_genesets_up) = paste0("markers_up_cluster", names(marker_genesets_up))
marker_genesets_down = split(markers_filt$down$gene, markers_filt$down$cluster)
names(marker_genesets_down) = paste0("markers_down_cluster", names(marker_genesets_down))
sc = ScAddLists(sc, lists=c(marker_genesets_up, marker_genesets_down), lists_slot="gene_lists")

if (markers_found) {
  p
} else {
  warning("No differentially expressed genes (cluster vs rest) found. The following related code is not executed, no related plots and tables are generated.")
}

Table of top marker genes

We use the term “marker genes” to specifically describe genes that are up-regulated in cells of one cluster compared to the rest.

if (markers_found) {
  markers_top = DegsUpDisplayTop(markers_filt$up, n=5)
  
  # Add labels
  markers_top$labels = paste0(markers_top$cluster, ": ", markers_top$gene)

  # Show table
  knitr::kable(markers_top %>% dplyr::select(-labels), align="l", caption="Up to top 5 marker genes per cell cluster") %>% 
    kableExtra::kable_styling(bootstrap_options=c("striped", "hover")) %>% 
    kableExtra::scroll_box(width="100%", height="700px") 
}

Up to top 5 marker genes per cell cluster

cluster	gene	avg_log2FC	p_val	p_val_adj	pct.1	pct.2
1	IL7R	2.701	2.0e-21	2.9e-17	0.634	0.071
1	LTB	2.041	1.6e-14	2.4e-10	0.604	0.111
1	RCAN3	1.125	5.8e-09	8.4e-05	0.446	0.121
1	CCR7	1.438	5.8e-08	8.3e-04	0.267	0.010
1	AQP3	1.101	2.8e-07	4.0e-03	0.277	0.020
2	NKG7	2.890	1.1e-33	1.6e-29	0.958	0.367
2	GZMH	2.470	2.1e-31	3.0e-27	0.903	0.094
2	GZMB	2.915	3.2e-26	4.6e-22	0.764	0.070
2	FGFBP2	3.060	5.1e-25	7.4e-21	0.806	0.094
2	KLRD1	2.229	4.8e-24	6.9e-20	0.833	0.156
3	LYZ	6.190	6.0e-34	8.6e-30	1.000	0.098
3	S100A9	5.475	2.7e-29	3.9e-25	0.963	0.017
3	CTSS	4.004	3.5e-29	5.0e-25	1.000	0.341
3	CLEC7A	2.536	4.9e-29	7.0e-25	1.000	0.046
3	FCN1	5.198	6.2e-29	8.9e-25	0.926	0.064

Visualisation of top marker genes

The following plots visualise the top marker genes for each cluster, respectively. Clear marker genes indicate good clusters that represent cell types.

# Note: We need to run this chunk as it specifies a variable that is used in chunk definitions below
if (markers_found) {

  # Feature plots and violin plots: each row contains 5 plots
  #   Row height is not dependent on the number of clusters
  #   The plot has 5 columns and 1 row per cluster, hence the layout works nicely if we find 
  #     at least 5 markers per cluster
  nr_rows_5cols = ceiling(nrow(markers_top)/5)
  fig_height_5cols = nr_rows_5cols * 3
  
  # Dotplots: each row contains 2 plots
  #   Row height is dependent on the number of clusters 
  nr_rows_dp_2cols = ceiling(length(levels(sc$seurat_clusters))/2)
  fig_height_dp_2cols = nr_rows_dp_2cols * max(4, 0.5 * length(levels(sc$seurat_clusters)))
  
} else {
  fig_height_5cols = fig_height_dp_2cols = 7 
}

Feature plots

if (markers_found) {
  # Plot each marker one by one, and then combine them all at the end
  p_list = list()
  for (i in 1:nrow(markers_top)) { 
    p_list[[i]] = Seurat::FeaturePlot(sc, features=markers_top$gene[i], 
                                      cols=c("lightgrey", param$col_clusters[markers_top$cluster[i]]),  
                                      combine=TRUE, label=TRUE) + 
      AddStyle(title=markers_top$labels[i], 
               xlab="", ylab="", 
               legend_position="bottom")
  }
  
  # Combine all plots
  p = patchwork::wrap_plots(p_list, ncol=5) + 
    patchwork::plot_annotation(title="UMAP, cells coloured by normalised gene expression data, top marker genes per cluster")
  p
}

Violin plots (normalised)

if (markers_found) {
  # Plot violin plots per marker gene, and combine it all at the end
  # This layout works out nicely if there are 5 marker genes per cluster
  p_list = list()
  for(i in 1:nrow(markers_top)) { 
    p_list[[i]] = Seurat::VlnPlot(sc, features=markers_top$gene[i], assay=param$assay_raw, pt.size=0, cols=param$col_clusters) + 
      AddStyle(title=markers_top$labels[i], xlab="")
  }
  p = patchwork::wrap_plots(p_list, ncol=5) + 
    patchwork::plot_annotation(title="Violin plot of for normalised gene expression data, top marker genes per cluster") & theme(legend.position="none")
  p
}

Dot plot (scaled)

if (markers_found) {
  # Visualises how feature expression changes across different clusters
  # Plot dotplots per cluster, and combine it all at the end
  p_list = lapply(markers_top$cluster %>% sort() %>% unique(), function(cl) {
    genes = markers_top %>% dplyr::filter(cluster==cl) %>% dplyr::pull(gene)
    p = suppressMessages(Seurat::DotPlot(sc, features=genes) + 
                           scale_colour_gradient2(low="steelblue", mid="lightgrey", high="darkgoldenrod1") +
                           AddStyle(title=paste0("Top marker genes for cluster ", cl, " (scaled)"), ylab="Cluster", legend_position="bottom") + 
                           theme(axis.text.x = element_text(angle=90, hjust=1, vjust=.5)) + 
                           guides(size=guide_legend(order=1)))
    return(p)
  })
  
  p = patchwork::wrap_plots(p_list, ncol=2) 
  p
}

Dot plot (non-scaled)

if (markers_found) {
  # Visualises how feature expression changes across different clusters
  # Plot dotplots per cluster, and combine it all at the end
  p_list = lapply(markers_top$cluster %>% sort() %>% unique(), function(cl) {
    genes = markers_top %>% dplyr::filter(cluster==cl) %>% dplyr::pull(gene)
    genes = genes[length(genes):1]
    p = suppressMessages(DotPlotUpdated(sc, features=genes, scale=FALSE, cols=c("lightgrey", param$col)) + 
                           AddStyle(title=paste0("Top marker genes for cluster ", cl, " (not scaled)"), ylab="Cluster", legend_position="bottom") + 
                           theme(axis.text.x = element_text(angle=90, hjust=1, vjust=.5)) + 
                           guides(size=guide_legend(order=1)))
    return(p)
  })
      
  p = patchwork::wrap_plots(p_list, ncol=2)
  p
}

Expression per cluster per sample

If the dataset contains multiple samples, we can visualise the expression of a gene that is up-regulated in a cluster separately for each sample. For each cluster, we extract up-regulated genes, and visualise expression of these genes in all cells in that cluster, split by their sample of origin.

fig_height_degs_per_cl = max(5, 
                             max(2, 0.3 * (sc$orig.ident %>% unique() %>% length())) * length(levels(sc$seurat_clusters)) * 2)

Scaled dotplots

First, we plot scaled expression as explained above (see section Known marker genes). This plot allows us to judge whether the expression of a gene is increased in one sample as compared to the other samples.

if (markers_found) {
  p_list = list()
  markers_filt_up_top = DegsUpDisplayTop(degs=markers_filt$up, n=50)
  for (cl in levels(sc$seurat_clusters)) {  
    markers_filt_up_cl_top = markers_filt_up_top %>% 
      dplyr::filter(cluster==cl) %>% 
      dplyr::pull(gene)

    if (length(markers_filt_up_cl_top) > 0) {
      p_list[[cl]] = suppressMessages(Seurat::DotPlot(sc, features=markers_filt_up_cl_top, idents=cl, group.by="orig.ident") +
        scale_colour_gradient2(low="steelblue", mid="lightgrey", high="darkgoldenrod1") + 
        AddStyle(title=paste0("Up to 50 markers (up-regulated genes) for cluster ", cl), ylab="Cluster", legend_position="bottom") + 
        theme(axis.text.x=element_text(angle=90, hjust=1, vjust=.5)) + 
        guides(size=guide_legend(order=1)))
    }
  }
  p = patchwork::wrap_plots(p_list, ncol=1) + patchwork::plot_annotation("Dotplot per cluster") 
  p
}

Non-scaled dotplots

Second, we plot normalised expression with no further scaling. This plot helps to get an impression of the total expression of a gene.

if (markers_found) {
  n_genes_max_dotplot = 50
  p_list = list()
  for (cl in levels(sc$seurat_clusters)) {            
    markers_filt_up_cl_top = markers_filt$up %>% 
      dplyr::filter(cluster==cl) %>% 
      dplyr::top_n(n=n_genes_max_dotplot, wt=p_val_adj_score) %>% 
      dplyr::pull(gene)
    if (length(markers_filt_up_cl_top) > 0) {
      p_list[[cl]] = DotPlotUpdated(sc, features=markers_filt_up_cl_top, idents=cl, group.by="orig.ident", scale=FALSE, cols=c("lightgrey", param$col)) +
        AddStyle(title=paste0("Up to ", n_genes_max_dotplot, " markers (up-regulated genes) for cluster ", cl, " (not scaled)"), ylab="Cluster", legend_position="bottom") + 
        theme(axis.text.x=element_text(angle=90, hjust=1, vjust=.5)) + 
        guides(size=guide_legend(order=1))
    }
  }
  
  p = patchwork::wrap_plots(p_list, ncol=1) + patchwork::plot_annotation("Dotplot per cluster (not scaled)") 
  p
}

Heatmaps

All up- and down-regulated genes

if (markers_found) {
  # This will sample 500 cells; the number of cells per seurat_cluster will be proportional
  cells_subset = ScSampleCells(sc, n=500, group="seurat_clusters", group_proportional=TRUE, seed=1)
    
  # Heatmap of all differentially expressed genes
  p = Seurat::DoHeatmap(sc, features=markers_filt$all$gene, group.colors=param$col_clusters, label=FALSE, cells=cells_subset) + 
    NoLegend() + 
    theme(axis.text.y=element_blank()) +
    ggtitle("Heatmap of scaled gene expression data, all genes differentially expressed between a cluster and the rest")
  p
}

Top 300 up-regulated genes

if (markers_found) {
  # This will sample 500 cells; the number of cells per seurat_cluster will be proportional
  cells_subset = ScSampleCells(sc, n=500, group="seurat_clusters", group_proportional=TRUE, seed=1)
  # With fig.height = 20, 300 features can be shown; distribute among clusters
  features_per_group = 300/length(levels(markers_filt$up$cluster))
  features_subset = markers_filt$up %>% 
    dplyr::group_by(cluster) %>% 
    dplyr::top_n(n=features_per_group, wt=avg_log2FC) %>% 
    dplyr::arrange(cluster, -avg_log2FC) %>%
    dplyr::pull(gene) %>%
    unique()
  
  # Heatmap of top up-regulated genes
  p = Seurat::DoHeatmap(sc, features=features_subset, group.colors=param$col_clusters, label=FALSE, cells=cells_subset) + 
    NoLegend() + 
    theme(axis.text.y=element_text(size=8)) +
    ggtitle("Heatmap of scaled gene expression data, top genes up-regulated in a cluster compared to the rest")
  p
}

Top 300 down-regulated genes

if (markers_found) {
  # This will sample 500 cells; the number of cells per seurat_cluster will be proportional
  cells_subset = ScSampleCells(sc, n=500, group="seurat_clusters", group_proportional=TRUE, seed=1)
  # With fig.height = 20, 300 features can be shown; distribute among clusters
  features_per_group = 300/length(levels(markers_filt$down$cluster))
  features_subset = markers_filt$down %>% 
    dplyr::group_by(cluster) %>% 
    dplyr::top_n(n=features_per_group, wt=-avg_log2FC) %>% 
    dplyr::arrange(cluster, avg_log2FC) %>%
    dplyr::pull(gene) %>%
    unique()
  
  # Heatmap of top down-regulated genes
  p = Seurat::DoHeatmap(sc, features=features_subset, group.colors=param$col_clusters, label=FALSE, cells=cells_subset) + 
    NoLegend() + 
    theme(axis.text.y=element_text(size=8)) +
    ggtitle("Heatmap of scaled gene expression data, top genes up-regulated in a cluster compared to the rest")
  p
}

Functional enrichment analysis

To gain first insights into potential functions of cells in a cluster, we test for over-representation of functional terms amongst up- and down-regulated genes of each cluster. Over-represented terms are written to file.

We first translate gene symbols of up- and down-regulated genes per cluster into Entrez gene symbols, and then use the “enrichR” R-package to access the “Enrichr” website https://amp.pharm.mssm.edu/Enrichr/. You can choose to test functional enrichment from a wide range of databases:

# Set Enrichr database
enrichR::setEnrichrSite(param$enrichr_site)

× (Message)
Connection changed to https://maayanlab.cloud/Enrichr/

× (Message)
Connection is Live!

# List databases
dbs_all = enrichR::listEnrichrDbs()
knitr::kable(dbs_all, align="l", caption="Enrichr databases") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover")) %>% 
  kableExtra::scroll_box(width="100%", height="300px")

Enrichr databases

geneCoverage	genesPerTerm	libraryName	link	numTerms	appyter	categoryId
13362	275	Genome_Browser_PWMs	http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database/	615	ea115789fcbf12797fd692cec6df0ab4dbc79c6a	1
27884	1284	TRANSFAC_and_JASPAR_PWMs	http://jaspar.genereg.net/html/DOWNLOAD/	326	7d42eb43a64a4e3b20d721fc7148f685b53b6b30	1
6002	77	Transcription_Factor_PPIs		290	849f222220618e2599d925b6b51868cf1dab3763	1
47172	1370	ChEA_2013	http://amp.pharm.mssm.edu/lib/cheadownload.jsp	353	7ebe772afb55b63b41b79dd8d06ea0fdd9fa2630	7
47107	509	Drug_Perturbations_from_GEO_2014	http://www.ncbi.nlm.nih.gov/geo/	701	ad270a6876534b7cb063e004289dcd4d3164f342	7
21493	3713	ENCODE_TF_ChIP-seq_2014	http://genome.ucsc.edu/ENCODE/downloads.html	498	497787ebc418d308045efb63b8586f10c526af51	7
1295	18	BioCarta_2013	https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways	249	4a293326037a5229aedb1ad7b2867283573d8bcd	7
3185	73	Reactome_2013	http://www.reactome.org/download/index.html	78	b343994a1b68483b0122b08650201c9b313d5c66	7
2854	34	WikiPathways_2013	http://www.wikipathways.org/index.php/Download_Pathways	199	5c307674c8b97e098f8399c92f451c0ff21cbf68	7
15057	300	Disease_Signatures_from_GEO_up_2014	http://www.ncbi.nlm.nih.gov/geo/	142	248c4ed8ea28352795190214713c86a39fd7afab	7
4128	48	KEGG_2013	http://www.kegg.jp/kegg/download/	200	eb26f55d3904cb0ea471998b6a932a9bf65d8e50	7
34061	641	TF-LOF_Expression_from_GEO	http://www.ncbi.nlm.nih.gov/geo/	269		1
7504	155	TargetScan_microRNA	http://www.targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_61	222	f4029bf6a62c91ab29401348e51df23b8c44c90f	7
16399	247	PPI_Hub_Proteins	http://amp.pharm.mssm.edu/X2K	385	69c0cfe07d86f230a7ef01b365abcc7f6e52f138	2
12753	57	GO_Molecular_Function_2015	http://www.geneontology.org/GO.downloads.annotations.shtml	1136	f531ac2b6acdf7587a54b79b465a5f4aab8f00f9	7
23726	127	GeneSigDB	https://pubmed.ncbi.nlm.nih.gov/22110038/	2139	6d655e0aa3408a7accb3311fbda9b108681a8486	4
32740	85	Chromosome_Location	http://software.broadinstitute.org/gsea/msigdb/index.jsp	386	8dab0f96078977223646ff63eb6187e0813f1433	7
13373	258	Human_Gene_Atlas	http://biogps.org/downloads/	84	0741451470203d7c40a06274442f25f74b345c9c	5
19270	388	Mouse_Gene_Atlas	http://biogps.org/downloads/	96	31191bfadded5f96983f93b2a113cf2110ff5ddb	5
13236	82	GO_Cellular_Component_2015	http://www.geneontology.org/GO.downloads.annotations.shtml	641	e1d004d5797cbd2363ef54b1c3b361adb68795c6	7
14264	58	GO_Biological_Process_2015	http://www.geneontology.org/GO.downloads.annotations.shtml	5192	bf120b6e11242b1a64c80910d8e89f87e618e235	7
3096	31	Human_Phenotype_Ontology	http://www.human-phenotype-ontology.org/	1779	17a138b0b70aa0e143fe63c14f82afb70bc3ed0a	3
22288	4368	Epigenomics_Roadmap_HM_ChIP-seq	http://www.roadmapepigenomics.org/	383	e1bc8a398e9b21f9675fb11bef18087eda21b1bf	1
4533	37	KEA_2013	http://amp.pharm.mssm.edu/lib/keacommandline.jsp	474	462045609440fa1e628a75716b81a1baa5bd9145	7
10231	158	NURSA_Human_Endogenous_Complexome	https://www.nursa.org/nursa/index.jsf	1796	7d3566b12ebc23dd23d9ca9bb97650f826377b16	2
2741	5	CORUM	http://mips.helmholtz-muenchen.de/genre/proj/corum/	1658	d047f6ead7831b00566d5da7a3b027ed9196e104	2
5655	342	SILAC_Phosphoproteomics	http://amp.pharm.mssm.edu/lib/keacommandline.jsp	84	54dcd9438b33301deb219866e162b0f9da7e63a0	2
10406	715	MGI_Mammalian_Phenotype_Level_3	http://www.informatics.jax.org/	71	c3bfc90796cfca8f60cba830642a728e23a53565	7
10493	200	MGI_Mammalian_Phenotype_Level_4	http://www.informatics.jax.org/	476	0b09a9a1aa0af4fc7ea22d34a9ae644d45864bd6	7
11251	100	Old_CMAP_up	http://www.broadinstitute.org/cmap/	6100	9041f90cccbc18479138330228b336265e09021c	4
8695	100	Old_CMAP_down	http://www.broadinstitute.org/cmap/	6100	ebc0d905b3b3142f936d400c5f2a4ff926c81c37	4
1759	25	OMIM_Disease	http://www.omim.org/downloads	90	cb2b92578a91e023d0498a334923ee84add34eca	4
2178	89	OMIM_Expanded	http://www.omim.org/downloads	187	27eca242904d8e12a38cf8881395bc50d57a03e1	4
851	15	VirusMINT	http://mint.bio.uniroma2.it/download.html	85	5abad1fc36216222b0420cadcd9be805a0dda63e	4
10061	106	MSigDB_Computational	http://www.broadinstitute.org/gsea/msigdb/collections.jsp	858	e4cdcc7e259788fdf9b25586cce3403255637064	4
11250	166	MSigDB_Oncogenic_Signatures	http://www.broadinstitute.org/gsea/msigdb/collections.jsp	189	c76f5319c33c4833c71db86a30d7e33cd63ff8cf	4
15406	300	Disease_Signatures_from_GEO_down_2014	http://www.ncbi.nlm.nih.gov/geo/	142	aabdf7017ae55ae75a004270924bcd336653b986	7
17711	300	Virus_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	323	45268b7fc680d05dd9a29743c2f2b2840a7620bf	4
17576	300	Virus_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	323	5f531580ccd168ee4acc18b02c6bdf8200e19d08	4
15797	176	Cancer_Cell_Line_Encyclopedia	https://portals.broadinstitute.org/ccle/home	967	eb38dbc3fb20adafa9d6f9f0b0e36f378e75284f	5
12232	343	NCI-60_Cancer_Cell_Lines	http://biogps.org/downloads/	93	75c81676d8d6d99d262c9660edc024b78cfb07c9	5
13572	301	Tissue_Protein_Expression_from_ProteomicsDB	https://www.proteomicsdb.org/	207		7
6454	301	Tissue_Protein_Expression_from_Human_Proteome_Map	http://www.humanproteomemap.org/index.php	30	49351dc989f9e6ca97c55f8aca7778aa3bfb84b9	5
3723	47	HMDB_Metabolites	http://www.hmdb.ca/downloads	3906	1905132115d22e4119bce543bdacaab074edb363	6
7588	35	Pfam_InterPro_Domains	ftp://ftp.ebi.ac.uk/pub/databases/interpro/	311	e2b4912cfb799b70d87977808c54501544e4cdc9	6
7682	78	GO_Biological_Process_2013	http://www.geneontology.org/GO.downloads.annotations.shtml	941	5216d1ade194ffa5a6c00f105e2b1899f64f45fe	7
7324	172	GO_Cellular_Component_2013	http://www.geneontology.org/GO.downloads.annotations.shtml	205	fd1332a42395e0bc1dba82868b39be7983a48cc5	7
8469	122	GO_Molecular_Function_2013	http://www.geneontology.org/GO.downloads.annotations.shtml	402	7e3e99e5aae02437f80b0697b197113ce3209ab0	7
13121	305	Allen_Brain_Atlas_up	http://www.brain-map.org/	2192	3804715a63a308570e47aa1a7877f01147ca6202	5
26382	1811	ENCODE_TF_ChIP-seq_2015	http://genome.ucsc.edu/ENCODE/downloads.html	816	56b6adb4dc8a2f540357ef992d6cd93dfa2907e5	1
29065	2123	ENCODE_Histone_Modifications_2015	http://genome.ucsc.edu/ENCODE/downloads.html	412	55b56cd8cf2ff04b26a09b9f92904008b82f3a6f	1
280	9	Phosphatase_Substrates_from_DEPOD	http://www.koehn.embl.de/depod/	59	d40701e21092b999f4720d1d2b644dd0257b6259	2
13877	304	Allen_Brain_Atlas_down	http://www.brain-map.org/	2192	ea67371adec290599ddf484ced2658cfae259304	5
15852	912	ENCODE_Histone_Modifications_2013	http://genome.ucsc.edu/ENCODE/downloads.html	109	c209ae527bc8e98e4ccd27a668d36cd2c80b35b4	7
4320	129	Achilles_fitness_increase	http://www.broadinstitute.org/achilles	216	98366496a75f163164106e72439fb2bf2f77de4e	4
4271	128	Achilles_fitness_decrease	http://www.broadinstitute.org/achilles	216	83a710c1ff67fd6b8af0d80fa6148c40dbd9bc64	4
10496	201	MGI_Mammalian_Phenotype_2013	http://www.informatics.jax.org/	476	a4c6e217a81a4a58ff5a1c9fc102b70beab298e9	7
1678	21	BioCarta_2015	https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways	239	70e4eb538daa7688691acfe5d9c3c19022be832b	7
756	12	HumanCyc_2015	http://humancyc.org/	125	711f0c02b23f5e02a01207174943cfeee9d3ea9c	7
3800	48	KEGG_2015	http://www.kegg.jp/kegg/download/	179	e80d25c56de53c704791ddfdc6ab5eec28ae7243	7
2541	39	NCI-Nature_2015	http://pid.nci.nih.gov/	209	47edfc012bcbb368a10b717d8dca103f7814b5a4	7
1918	39	Panther_2015	http://www.pantherdb.org/	104	ab824aeeff0712bab61f372e43aebb870d1677a9	7
5863	51	WikiPathways_2015	http://www.wikipathways.org/index.php/Download_Pathways	404	1f7eea2f339f37856522c1f1c70ec74c7b25325f	7
6768	47	Reactome_2015	http://www.reactome.org/download/index.html	1389	36e541bee015eddb8d53827579549e30fe7a3286	7
25651	807	ESCAPE	http://www.maayanlab.net/ESCAPE/	315	a7acc741440264717ff77751a7e5fed723307835	5
19129	1594	HomoloGene	http://www.ncbi.nlm.nih.gov/homologene	12	663b665b75a804ef98add689f838b68e612f0d2a	6
23939	293	Disease_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	839	0f412e0802d76efa0374504c2c9f5e0624ff7f09	8
23561	307	Disease_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	839	9ddc3902fb01fb9eaf1a2a7c2ff3acacbb48d37e	8
23877	302	Drug_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	906	068623a05ecef3e4a5e0b4f8db64bb8faa3c897f	8
15886	9	Genes_Associated_with_NIH_Grants	https://grants.nih.gov/grants/oer.htm	32876	76fc5ec6735130e287e62bae6770a3c5ee068645	6
24350	299	Drug_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	906	c9c2155b5ac81ac496854fa61ba566dcae06cc80	8
3102	25	KEA_2015	http://amp.pharm.mssm.edu/Enrichr	428	18a081774e6e0aaf60b1a4be7fd20afcf9e08399	2
31132	298	Gene_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	2460	53dedc29ce3100930d68e506f941ef59de05dc6b	8
30832	302	Gene_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	2460	499882af09c62dd6da545c15cb51c1dc5e234f78	8
48230	1429	ChEA_2015	http://amp.pharm.mssm.edu/Enrichr	395	712eb7b6edab04658df153605ec6079fa89fb5c7	7
5613	36	dbGaP	http://www.ncbi.nlm.nih.gov/gap	345	010f1267055b1a1cb036e560ea525911c007a666	4
9559	73	LINCS_L1000_Chem_Pert_up	https://clue.io/	33132	5e678b3debe8d8ea95187d0cd35c914017af5eb3	4
9448	63	LINCS_L1000_Chem_Pert_down	https://clue.io/	33132	fedbf5e221f45ee60ebd944f92569b5eda7f2330	4
16725	1443	GTEx_Tissue_Expression_Down	http://www.gtexportal.org/	2918	74b818bd299a9c42c1750ffe43616aa9f7929f02	5
19249	1443	GTEx_Tissue_Expression_Up	http://www.gtexportal.org/	2918	103738763d89cae894bec9f145ac28167a90e611	5
15090	282	Ligand_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	261	1eb3c0426140340527155fd0ef67029db2a72191	8
16129	292	Aging_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	286	cd95fe1b505ba6f28cd722cfba50fdea979d3b4c	8
15309	308	Aging_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	286	74c4f0a0447777005b2a5c00c9882a56dfc62d7c	8
15103	318	Ligand_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	261	31baa39da2931ddd5f7aedf2d0bbba77d2ba7b46	8
15022	290	MCF7_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	401	555f68aef0a29a67b614a0d7e20b6303df9069c6	8
15676	310	MCF7_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	401	1bc2ba607f1ff0dda44e2a15f32a2c04767da18c	8
15854	279	Microbe_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	312	9e613dba78ef7e60676b13493a9dc49ccd3c8b3f	8
15015	321	Microbe_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	312	d0c3e2a68e8c611c669098df2c87b530cec3e132	8
3788	159	LINCS_L1000_Ligand_Perturbations_down	https://clue.io/	96	957846cb05ef31fc8514120516b73cc65af7980e	4
3357	153	LINCS_L1000_Ligand_Perturbations_up	https://clue.io/	96	3bd494146c98d8189898a947f5ef5710f1b7c4b2	4
12668	300	L1000_Kinase_and_GPCR_Perturbations_down	https://clue.io/	3644	1ccc5bce553e0c2279f8e3f4ddcfbabcf566623b	2
12638	300	L1000_Kinase_and_GPCR_Perturbations_up	https://clue.io/	3644	b54a0d4ba525eac4055c7314ca9d9312adcb220c	2
8973	64	Reactome_2016	http://www.reactome.org/download/index.html	1530	1f54638e8f45075fb79489f0e0ef906594cb0678	2
7010	87	KEGG_2016	http://www.kegg.jp/kegg/download/	293	43f56da7540195ba3c94eb6e34c522a699b36da9	7
5966	51	WikiPathways_2016	http://www.wikipathways.org/index.php/Download_Pathways	437	340be98b444cad50bb974df69018fd598e23e5e1	7
15562	887	ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X		104	5426f7747965c23ef32cff46fabf906e2cd76bfa	1
17850	300	Kinase_Perturbations_from_GEO_down	http://www.ncbi.nlm.nih.gov/geo/	285	bb9682d78b8fc43be842455e076166fcd02cefc3	2
17660	300	Kinase_Perturbations_from_GEO_up	http://www.ncbi.nlm.nih.gov/geo/	285	78618915009cac3a0663d6f99d359e39a31b6660	2
1348	19	BioCarta_2016	http://cgap.nci.nih.gov/Pathways/BioCarta_Pathways	237	13d9ab18921d5314a5b2b366f6142b78ab0ff6aa	2
934	13	HumanCyc_2016	http://humancyc.org/	152	d6a502ef9b4c789ed5e73ca5a8de372796e5c72a	2
2541	39	NCI-Nature_2016	http://pid.nci.nih.gov/	209	3c1e1f7d1a651d9aaa198e73704030716fc09431	2
2041	42	Panther_2016	http://www.pantherdb.org/pathway/	112	ca5f6abf7f75d9baae03396e84d07300bf1fd051	2
5209	300	DrugMatrix	https://ntp.niehs.nih.gov/drugmatrix/	7876	255c3db820d612f34310f22a6985dad50e9fe1fe	4
49238	1550	ChEA_2016	http://amp.pharm.mssm.edu/Enrichr	645	af271913344aa08e6a755af1d433ef15768d749a	1
2243	19	huMAP	http://proteincomplexes.org/	995	249247d2f686d3eb4b9e4eb976c51159fac80a89	2
19586	545	Jensen_TISSUES	http://tissues.jensenlab.org/	1842	e8879ab9534794721614d78fe2883e9e564d7759	3
22440	505	RNA-Seq_Disease_Gene_and_Drug_Signatures_from_GEO	http://www.ncbi.nlm.nih.gov/geo/	1302	f0752e4d7f5198f86446678966b260c530d19d78	8
8184	24	MGI_Mammalian_Phenotype_2017	http://www.informatics.jax.org/	5231	0705e59bff98deda6e9cbe00cfcdd871c85e7d04	7
18329	161	Jensen_COMPARTMENTS	http://compartments.jensenlab.org/	2283	56ec68c32d4e83edc2ee83bea0e9f6a3829b2279	3
15755	28	Jensen_DISEASES	http://diseases.jensenlab.org/	1811	3045dff8181367c1421627bb8e4c5a32c6d67f98	3
10271	22	BioPlex_2017	http://bioplex.hms.harvard.edu/	3915	b8620b1a9d0d271d1a2747d8cfc63589dba39991	2
10427	38	GO_Cellular_Component_2017	http://www.geneontology.org/	636	8fed21d22dfcc3015c05b31d942fdfc851cc8e04	7
10601	25	GO_Molecular_Function_2017	http://www.geneontology.org/	972	b4018906e0a8b4e81a1b1afc51e0a2e7655403eb	7
13822	21	GO_Biological_Process_2017	http://www.geneontology.org/	3166	d9da4dba4a3eb84d4a28a3835c06dfbbe5811f92	7
8002	143	GO_Cellular_Component_2017b	http://www.geneontology.org/	816	ecf39c41fa5bc7deb625a2b5761a708676e9db7c	7
10089	45	GO_Molecular_Function_2017b	http://www.geneontology.org/	3271	8d8340361dd36a458f1f0a401f1a3141de1f3200	7
13247	49	GO_Biological_Process_2017b	http://www.geneontology.org/	10125	6404c38bffc2b3732de4e3fbe417b5043009fe34	7
21809	2316	ARCHS4_Tissues	http://amp.pharm.mssm.edu/archs4	108	4126374338235650ab158ba2c61cd2e2383b70df	5
23601	2395	ARCHS4_Cell-lines	http://amp.pharm.mssm.edu/archs4	125	5496ef9c9ae9429184d0b9485c23ba468ee522a8	5
20883	299	ARCHS4_IDG_Coexp	http://amp.pharm.mssm.edu/archs4	352	ce60be284fdd5a9fc6240a355421a9e12b1ee84a	4
19612	299	ARCHS4_Kinases_Coexp	http://amp.pharm.mssm.edu/archs4	498	6721c5ed97b7772e4a19fdc3f797110df0164b75	2
25983	299	ARCHS4_TFs_Coexp	http://amp.pharm.mssm.edu/archs4	1724	8a468c3ae29fa68724f744cbef018f4f3b61c5ab	1
19500	137	SysMyo_Muscle_Gene_Sets	http://sys-myo.rhcloud.com/	1135		8
14893	128	miRTarBase_2017	http://mirtarbase.mbc.nctu.edu.tw/	3240	6b7c7fe2a97b19aecbfba12d8644af6875ad99c4	1
17598	1208	TargetScan_microRNA_2017	http://www.targetscan.org/	683	79d13fb03d2fa6403f9be45c90eeda0f6822e269	1
5902	109	Enrichr_Libraries_Most_Popular_Genes	http://amp.pharm.mssm.edu/Enrichr	121	e9b7d8ee237d0a690bd79d970a23a9fa849901ed	6
12486	299	Enrichr_Submissions_TF-Gene_Coocurrence	http://amp.pharm.mssm.edu/Enrichr	1722	be2ca8ef5a8c8e17d7e7bd290e7cbfe0951396c0	1
1073	100	Data_Acquisition_Method_Most_Popular_Genes	http://amp.pharm.mssm.edu/Enrichr	12	17ce5192b9eba7d109b6d228772ea8ab222e01ef	6
19513	117	DSigDB	http://tanlab.ucdenver.edu/DSigDB/DSigDBv1.0/	4026	287476538ab98337dbe727b3985a436feb6d192a	4
14433	36	GO_Biological_Process_2018	http://www.geneontology.org/	5103	b5b77681c46ac58cd050e60bcd4ad5041a9ab0a9	7
8655	61	GO_Cellular_Component_2018	http://www.geneontology.org/	446	e9ebe46188efacbe1056d82987ff1c70218fa7ae	7
11459	39	GO_Molecular_Function_2018	http://www.geneontology.org/	1151	79ff80ae9a69dd00796e52569e41422466fa0bee	7
19741	270	TF_Perturbations_Followed_by_Expression	http://www.ncbi.nlm.nih.gov/geo/	1958	34d08a4878c19584aaf180377f2ea96faa6a6eb1	1
27360	802	Chromosome_Location_hg19	http://hgdownload.cse.ucsc.edu/downloads.html	36	fdab39c467ba6b0fb0288df1176d7dfddd7196d5	6
13072	26	NIH_Funded_PIs_2017_Human_GeneRIF	https://www.ncbi.nlm.nih.gov/pubmed/	5687	859b100fac3ca774ad84450b1fbb65a78fcc6b12	6
13464	45	NIH_Funded_PIs_2017_Human_AutoRIF	https://www.ncbi.nlm.nih.gov/pubmed/	12558	fc5bf033b932cf173633e783fc8c6228114211f8	6
13787	200	Rare_Diseases_AutoRIF_ARCHS4_Predictions	https://amp.pharm.mssm.edu/geneshot/	3725	375ff8cdd64275a916fa24707a67968a910329bb	4
13929	200	Rare_Diseases_GeneRIF_ARCHS4_Predictions	https://www.ncbi.nlm.nih.gov/gene/about-generif	2244	0f7fb7f347534779ecc6c87498e96b5460a8d652	4
16964	200	NIH_Funded_PIs_2017_AutoRIF_ARCHS4_Predictions	https://www.ncbi.nlm.nih.gov/pubmed/	12558	f77de51aaf0979dd6f56381cf67ba399b4640d28	6
17258	200	NIH_Funded_PIs_2017_GeneRIF_ARCHS4_Predictions	https://www.ncbi.nlm.nih.gov/pubmed/	5684	25fa899b715cd6a9137f6656499f89cd25144029	6
10352	58	Rare_Diseases_GeneRIF_Gene_Lists	https://www.ncbi.nlm.nih.gov/gene/about-generif	2244	0fb9ac92dbe52024661c088f71a1134f00567a8b	4
10471	76	Rare_Diseases_AutoRIF_Gene_Lists	https://amp.pharm.mssm.edu/geneshot/	3725	ee3adbac2da389959410260b280e7df1fd3730df	4
12419	491	SubCell_BarCode	http://www.subcellbarcode.org/	104	b50bb9480d8a77103fb75b331fd9dd927246939a	2
19378	37	GWAS_Catalog_2019	https://www.ebi.ac.uk/gwas	1737	fef3864bcb5dd9e60cee27357eff30226116c49b	4
6201	45	WikiPathways_2019_Human	https://www.wikipathways.org/	472	b0c9e9ebb9014f14561e896008087725a2db24b7	7
4558	54	WikiPathways_2019_Mouse	https://www.wikipathways.org/	176	e7750958da20f585c8b6d5bc4451a5a4305514ba	7
3264	22	TRRUST_Transcription_Factors_2019	https://www.grnpedia.org/trrust/	571	5f8cf93e193d2bcefa5a37ccdf0eefac576861b0	1
7802	92	KEGG_2019_Human	https://www.kegg.jp/	308	3477bc578c4ea5d851dcb934fe2a41e9fd789bb4	7
8551	98	KEGG_2019_Mouse	https://www.kegg.jp/	303	187eb44b2d6fa154ebf628eba1f18537f64e797c	7
12444	23	InterPro_Domains_2019	https://www.ebi.ac.uk/interpro/	1071	18dd5ec520fdf589a93d6a7911289c205e1ddf22	6
9000	20	Pfam_Domains_2019	https://pfam.xfam.org/	608	a6325ed264f9ac9e6518796076c46a1d885cca7a	6
7744	363	DepMap_WG_CRISPR_Screens_Broad_CellLines_2019	https://depmap.org/	558	0b08b32b20854ac8a738458728a9ea50c2e04800	4
6204	387	DepMap_WG_CRISPR_Screens_Sanger_CellLines_2019	https://depmap.org/	325	b7c4ead26d0eb64f1697c030d31682b581c8bb56	4
13420	32	MGI_Mammalian_Phenotype_Level_4_2019	http://www.informatics.jax.org/	5261	f1bed632e89ebc054da44236c4815cdce03ef5ee	7
14148	122	UK_Biobank_GWAS_v1	https://www.ukbiobank.ac.uk/tag/gwas/	857	958fb52e6215626673a5acf6e9289a1b84d11b4a	4
9813	49	BioPlanet_2019	https://tripod.nih.gov/bioplanet/	1510	e110851dfc763d30946f2abedcc2cd571ac357a0	2
1397	13	ClinVar_2019	https://www.ncbi.nlm.nih.gov/clinvar/	182	0a95303f8059bec08836ecfe02ce3da951150547	4
9116	22	PheWeb_2019	http://pheweb.sph.umich.edu/	1161	6a7c7321b6b72c5285b722f7902d26a2611117cb	4
17464	63	DisGeNET	https://www.disgenet.org	9828	3c261626478ce9e6bf2c7f0a8014c5e901d43dc0	4
394	73	HMS_LINCS_KinomeScan	http://lincs.hms.harvard.edu/kinomescan/	148	47ba06cdc92469ac79400fc57acd84ba343ba616	2
11851	586	CCLE_Proteomics_2020	https://portals.broadinstitute.org/ccle	378	7094b097ae2301a1d6a5bd856a193b084cca993d	5
8189	421	ProteomicsDB_2020	https://www.proteomicsdb.org/	913	8c87c8346167bac2ba68195a32458aba9b1acfd1	5
18704	100	lncHUB_lncRNA_Co-Expression	https://amp.pharm.mssm.edu/lnchub/	3729	45b597d7efa5693b7e4172b09c0ed2dda3305582	1
5605	39	Virus-Host_PPI_P-HIPSTer_2020	http://phipster.org/	6715	a592eed13e8e9496aedbab63003b965574e46a65	2
5718	31	Elsevier_Pathway_Collection	http://www.transgene.ru/disease-pathways/	1721	9196c760e3bcae9c9de1e3f87ad81f96bde24325	2
14156	40	Table_Mining_of_CRISPR_Studies		802	ad580f3864fa8ff69eaca11f6d2e7f9b86378d08	6
16979	295	COVID-19_Related_Gene_Sets	https://amp.pharm.mssm.edu/covid19	205	72b0346849570f66a77a6856722601e711596cb4	7
4383	146	MSigDB_Hallmark_2020	https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp	50	6952efda94663d4bd8db09bf6eeb4e67d21ef58c	2
54974	483	Enrichr_Users_Contributed_Lists_2020	https://maayanlab.cloud/Enrichr	1482	8dc362703b38b30ac3b68b6401a9b20a58e7d3ef	6
12118	448	TG_GATES_2020	https://toxico.nibiohn.go.jp/english/	1190	9e32560437b11b4628b00ccf3d584360f7f7daee	4
12361	124	Allen_Brain_Atlas_10x_scRNA_2021	https://portal.brain-map.org/	766	46f8235cb585829331799a71aec3f7c082170219	5
9763	139	Descartes_Cell_Types_and_Tissue_2021	https://descartes.brotmanbaty.org/bbi/human-gene-expression-during-development/	172		5
8078	102	KEGG_2021_Human	https://www.kegg.jp/	320		2
7173	43	WikiPathway_2021_Human	https://www.wikipathways.org/	622		2
5833	100	HuBMAP_ASCT_plus_B_augmented_w_RNAseq_Coexpression	https://hubmapconsortium.github.io/ccf-asct-reporter/	344		5
14937	33	GO_Biological_Process_2021	http://www.geneontology.org/	6036		3
11497	80	GO_Cellular_Component_2021	http://www.geneontology.org/	511		3
11936	34	GO_Molecular_Function_2021	http://www.geneontology.org/	1274		3
9767	33	MGI_Mammalian_Phenotype_Level_4_2021	http://www.informatics.jax.org/	4601		3
14167	80	CellMarker_Augmented_2021	http://biocc.hrbmu.edu.cn/CellMarker/	1097		5
17851	102	Orphanet_Augmented_2021	http://www.orphadata.org/	3774		4
16853	360	COVID-19_Related_Gene_Sets_2021	https://maayanlab.cloud/covid19/	478		4
6654	136	PanglaoDB_Augmented_2021	https://panglaodb.se/	178		5
1683	10	Azimuth_Cell_Types_2021	https://azimuth.hubmapconsortium.org/	341		5
20414	112	PhenGenI_Association_2021	https://www.ncbi.nlm.nih.gov/gap/phegeni	950		4
26076	250	RNAseq_Automatic_GEO_Signatures_Human_Down	https://maayanlab.cloud/archs4/	4269		8
26338	250	RNAseq_Automatic_GEO_Signatures_Human_Up	https://maayanlab.cloud/archs4/	4269		8
25381	250	RNAseq_Automatic_GEO_Signatures_Mouse_Down	https://maayanlab.cloud/archs4/	4216		8
25409	250	RNAseq_Automatic_GEO_Signatures_Mouse_Up	https://maayanlab.cloud/archs4/	4216		8
11980	250	GTEx_Aging_Signatures_2021	https://gtexportal.org/	270		4
31158	805	HDSigDB_Human_2021	https://hdsigdb.hdinhd.org/	2564		4
30006	815	HDSigDB_Mouse_2021	https://hdsigdb.hdinhd.org/	2579		4

markers_enriched=list()

if (markers_found) {
  # Upregulated markers
  
  # Convert Seurat names of upregulated marker per cluster to Entrez; use named lists for translation
  # Is that still neccessary?
  marker_genesets_up = sapply(levels(sc$seurat_clusters), function(x) {
    tmp = markers_filt$up %>% dplyr::filter(cluster==x) %>% dplyr::pull(gene)
    tmp = sapply(tmp, function(n) seurat_rowname_to_entrez[[n]][1], USE.NAMES=TRUE, simplify=TRUE) %>% unlist() %>% as.character()
    return(tmp[!is.na(tmp)])
  }, USE.NAMES=TRUE, simplify=TRUE)
  
  # Tests done by Enrichr
  marker_genesets_up_enriched = purrr::map(marker_genesets_up, EnrichrTest, databases=param$enrichr_dbs, padj=param$enrichr_padj)
  marker_genesets_up_enriched = purrr::map(list_names(marker_genesets_up_enriched), function(n) {
    return(purrr::map(marker_genesets_up_enriched[[n]], function(d){
      return(cbind(d, Cluster=rep(n, nrow(d)), Direction=rep("up", nrow(d))))
    }))
  })

  # Write to files
  invisible(purrr::map(names(marker_genesets_up_enriched), function(n) {
    EnrichrWriteResults(enrichr_results=marker_genesets_up_enriched[[n]],
                        file=file.path(param$path_out, "marker_degs", paste0("functions_marker_up_cluster_", n, "_vs_rest.xlsx")))
  }))
  
  
  # Downregulated markers
  
  # Convert Seurat names of downregulated marker per cluster to Entrez; use named lists for translation
  # Is that still neccessary?
  marker_genesets_down = sapply(levels(sc$seurat_clusters), function(x) {
    tmp = markers_filt$down %>% dplyr::filter(cluster==x) %>% dplyr::pull(gene)
    tmp = sapply(tmp, function(x) seurat_rowname_to_entrez[[x]][1], USE.NAMES=TRUE, simplify=TRUE) %>% unlist() %>% as.character()
    return(tmp[!is.na(tmp)])
  }, USE.NAMES=TRUE, simplify=TRUE)
  
  #  Tests done by Enrichr
  marker_genesets_down_enriched = purrr::map(marker_genesets_down, EnrichrTest, databases=param$enrichr_dbs, padj=param$enrichr_padj)
  marker_genesets_down_enriched = purrr::map(list_names(marker_genesets_down_enriched), function(n) {
    return(purrr::map(marker_genesets_down_enriched[[n]], function(d){
      return(cbind(d, Cluster=rep(n, nrow(d)), Direction=rep("down", nrow(d))))
    }))
  })
  
  # Write to files
  invisible(purrr::map(names(marker_genesets_down_enriched), function(n) {
    EnrichrWriteResults(enrichr_results=marker_genesets_down_enriched[[n]],
                        file=file.path(param$path_out, "marker_degs", paste0("functions_marker_down_cluster_", n, "_vs_rest.xlsx")))
  }))
  
  # Combine, flatten into data.frame and add to sc misc slot
  marker_genesets_enriched = c(marker_genesets_up_enriched, marker_genesets_down_enriched)
  marker_genesets_enriched = unname(marker_genesets_enriched)
  marker_genesets_enriched = purrr::map(marker_genesets_enriched, FlattenEnrichr) %>% dplyr::bind_rows()
  marker_genesets_enriched$Cluster = factor(marker_genesets_enriched$Cluster, levels=levels(sc$seurat_clusters))
  marker_genesets_enriched$Direction = factor(marker_genesets_enriched$Direction, levels=c("up", "down"))
  
  misc_content = Misc(sc, "markers")
  misc_content[["enrichr"]] = marker_genesets_enriched
  suppressWarnings({Misc(sc, "markers") = misc_content})
}

The following table contains the top enriched terms per cluster.

# Top enriched terms (TODO: better plots, functions)
if (markers_found) {
  cat("#### {.tabset} \n \n")
  
  # Get top ten up and down over all databases per cluster
  marker_genesets_top_enriched = marker_genesets_enriched %>% dplyr::group_by(Cluster, Direction) %>%
    dplyr::top_n(n=10, wt=Combined.Score)
  
  # Print as tabs
  for(n in levels(marker_genesets_top_enriched$Cluster)){
    cat("##### ", n, " \n")
    
    print(knitr::kable(marker_genesets_top_enriched %>% dplyr::ungroup() %>% dplyr::filter(Cluster==n) %>% dplyr::select(Database, Term, Direction, Adjusted.P.value, Odds.Ratio, Combined.Score), 
                     align="l", caption="Top ten enriched terms per geneset", format="html") %>% 
    kableExtra::kable_styling(bootstrap_options=c("striped", "hover")) %>% 
    kableExtra::scroll_box(width="100%", height="700px"))

    cat(" \n \n")
  }
  cat(" \n \n")
}

1

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio	Combined.Score
Azimuth_Cell_Types_2021	CD4+ Central Memory T CL0000897	up	0.00e+00	2855.2857	57494.919
Azimuth_Cell_Types_2021	CD4+ Central Memory T 3 CL0000897	up	0.00e+00	2855.2857	57494.919
Azimuth_Cell_Types_2021	CD4+ Effector Memory T CL0001044	up	0.00e+00	2855.2857	57494.919
Azimuth_Cell_Types_2021	CD4+ Effector Memory T 3 CL0001044	up	0.00e+00	2855.2857	57494.919
Azimuth_Cell_Types_2021	CD4+ Naive T CL0000895	up	1.00e-07	1248.6250	22535.824
Azimuth_Cell_Types_2021	CD4+ Central Memory T 1 CL0000897	up	1.14e-05	1249.1250	15739.235
Azimuth_Cell_Types_2021	CD4+ T Cell CL0000624	up	1.14e-05	1249.1250	15739.235
Azimuth_Cell_Types_2021	CD8+ Central Memory T 2 CL0000909	up	1.14e-05	1249.1250	15739.235
CellMarker_Augmented_2021	Naive T cell:Undefined	up	6.77e-05	3331.8333	46990.970
CellMarker_Augmented_2021	Central Memory T cell:Undefined	up	6.77e-05	1998.9000	26709.216
CellMarker_Augmented_2021	Effector Memory T cell:Undefined	up	6.77e-05	1998.9000	26709.216
CellMarker_Augmented_2021	T Helper cell:Kidney	up	7.25e-05	1249.1250	15739.235
Azimuth_Cell_Types_2021	Monocyte CL0000576	down	0.00e+00	3385.5283	170276.989
Azimuth_Cell_Types_2021	CD56-dim Natural Killer CL0000939	down	0.00e+00	1476.7407	63321.835
Azimuth_Cell_Types_2021	Natural Killer Cell CL0000623	down	0.00e+00	1476.7407	63321.835
Azimuth_Cell_Types_2021	CD56-dim Natural Killer 2 CL0000939	down	0.00e+00	845.7273	30450.396
Azimuth_Cell_Types_2021	Natural Killer CL0000623	down	0.00e+00	507.3855	17313.347
Azimuth_Cell_Types_2021	CD56-dim Natural Killer 1 CL0000939	down	0.00e+00	533.9464	15788.644
CellMarker_Augmented_2021	CD4+ Cytotoxic T cell:Liver	down	0.00e+00	209.8184	20913.563
CellMarker_Augmented_2021	Cytotoxic T cell:Peripheral Blood	down	0.00e+00	140.0385	11360.761
CellMarker_Augmented_2021	Cytotoxic T cell:Blood	down	0.00e+00	140.0385	11360.761
CellMarker_Augmented_2021	Epithelial cell:Bone Marrow	down	0.00e+00	125.5637	9512.812
CellMarker_Augmented_2021	Neutrophil:Pluripotent Stem Cell	down	0.00e+00	125.5637	9512.812

2

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio	Combined.Score
Azimuth_Cell_Types_2021	Natural Killer Cell CL0000623	up	0.0000000	1606.0575	64367.237
Azimuth_Cell_Types_2021	Natural Killer CL0000623	up	0.0000000	963.5379	36800.589
Azimuth_Cell_Types_2021	CD4+ Cytotoxic T CL0000934	up	0.0000000	802.9080	30045.818
Azimuth_Cell_Types_2021	CD56-dim Natural Killer CL0000939	up	0.0000000	998.0000	32951.243
Azimuth_Cell_Types_2021	CD56-dim Natural Killer 1 CL0000939	up	0.0000000	998.0000	32951.243
Azimuth_Cell_Types_2021	CD56-dim Natural Killer 2 CL0000939	up	0.0000000	998.0000	32951.243
Azimuth_Cell_Types_2021	CD8+ Effector Memory T CL0001050	up	0.0000000	998.0000	32951.243
Azimuth_Cell_Types_2021	CD8+ Effector Memory T 4 CL0001050	up	0.0000000	998.0000	32951.243
CellMarker_Augmented_2021	CD4+ Cytotoxic T cell:Liver	up	0.0000000	821.3111	103087.081
CellMarker_Augmented_2021	Effector CD8+ Memory T (Tem) cell:Peripheral Blood	up	0.0000000	310.9375	24277.333
Azimuth_Cell_Types_2021	CD4+ Central Memory T CL0000897	down	0.0000335	713.6786	8368.259
Azimuth_Cell_Types_2021	CD4+ Central Memory T 1 CL0000897	down	0.0000335	713.6786	8368.259
Azimuth_Cell_Types_2021	CD4+ Central Memory T 3 CL0000897	down	0.0000335	713.6786	8368.259
Azimuth_Cell_Types_2021	CD4+ Effector Memory T CL0001044	down	0.0000335	713.6786	8368.259
Azimuth_Cell_Types_2021	CD4+ Effector Memory T 3 CL0001044	down	0.0000335	713.6786	8368.259
Azimuth_Cell_Types_2021	CD4+ T Cell CL0000624	down	0.0000335	713.6786	8368.259
Azimuth_Cell_Types_2021	CD8+ Central Memory T 2 CL0000909	down	0.0000335	713.6786	8368.259
Azimuth_Cell_Types_2021	CD4+ Naive T CL0000895	down	0.0001111	335.6975	3488.775
CellMarker_Augmented_2021	Naive T cell:Undefined	down	0.0404588	624.5938	3808.550
CellMarker_Augmented_2021	Sertoli cell:Testis	down	0.0404588	624.5938	3808.550

3

Top ten enriched terms per geneset

Database	Term	Direction	Adjusted.P.value	Odds.Ratio	Combined.Score
Azimuth_Cell_Types_2021	CD14+ Monocyte CL0002057	up	0e+00	498.01083	27278.74
Azimuth_Cell_Types_2021	Monocyte CL0000576	up	0e+00	197090.00000	8367771.67
CellMarker_Augmented_2021	Monocyte:Fetal Kidney	up	0e+00	82.88305	44341.02
CellMarker_Augmented_2021	Monocyte:Lung	up	0e+00	116.56305	21722.04
CellMarker_Augmented_2021	Monocyte:Undefined	up	0e+00	111.25341	20546.56
CellMarker_Augmented_2021	Monocyte:Liver	up	0e+00	111.40549	20233.75
CellMarker_Augmented_2021	Macrophage:Synovium	up	0e+00	111.40549	20233.75
CellMarker_Augmented_2021	Monocyte:Plasma	up	0e+00	104.15784	18027.61
CellMarker_Augmented_2021	Monocyte:Synovium	up	0e+00	104.15784	18027.61
CellMarker_Augmented_2021	Classical monocyte:Blood	up	0e+00	97.39516	16332.62
CellMarker_Augmented_2021	Intermediate monocyte:Blood	up	0e+00	97.39516	16332.62
CellMarker_Augmented_2021	Non-classical monocyte:Blood	up	0e+00	97.39516	16332.62
CellMarker_Augmented_2021	CD14++CD16- monocyte:Blood	up	0e+00	97.39516	16332.62
Azimuth_Cell_Types_2021	CD8+ Memory/Effector T CL0000625	down	0e+00	907.63636	25328.60
Azimuth_Cell_Types_2021	CD8 T CL0000625	down	0e+00	907.63636	25328.60
CellMarker_Augmented_2021	Natural Killer T (NKT) cell:Fetal Kidney	down	0e+00	417339.00000	16726332.42
CellMarker_Augmented_2021	T cell:Thyroid	down	1e-07	1248.18750	22014.12
CellMarker_Augmented_2021	CD4+ T cell:Blood	down	1e-07	1248.18750	22014.12
CellMarker_Augmented_2021	T Helper cell:Peripheral Blood	down	1e-07	1248.18750	22014.12
CellMarker_Augmented_2021	T cell:Blood	down	1e-07	1248.18750	22014.12
CellMarker_Augmented_2021	CD4+ T Helper cell:Peripheral Blood	down	1e-07	1248.18750	22014.12
CellMarker_Augmented_2021	T cell:Lymphoid Tissue	down	1e-07	1248.18750	22014.12
CellMarker_Augmented_2021	Infiltrated Mononuclear cell:Kidney	down	1e-07	1248.18750	22014.12
CellMarker_Augmented_2021	Angiogenic T cell:Blood	down	1e-07	1248.18750	22014.12

Differentially expressed genes

If requested, we identify genes that are differentially expressed between two groups of cells. Groups can be defined by columns in the cell metadata. Different types of tests can be used and input data for testing can be the different assays as well as the computed dimensionality reductions. Resulting p-values are adjusted using the Bonferroni method. The names of differentially expressed genes per cluster, alongside statistical measures and additional gene annotation are written to file.

# We first compute the DEGs for all requested contrasts

# Prepare a list with contrasts (input can be R data.frame table or Excel file)
degs_contrasts_list = DegsSetupContrastsList(sc, param$deg_contrasts, param$latent_vars)

# Add the actual data to the list
degs_contrasts_list = purrr::map(degs_contrasts_list, function(contrast){
  # If there were already errors, just return
  if (length(contrast[["error_messages"]]) > 0) return(c(contrast, list(object=NULL, cells_group1_idx_subset=as.integer(), cells_group2_idx_subset=as.integer())))
  
  # Get cells indices
  cells_group1_idx = contrast[["cells_group1_idx"]]
  cells_group2_idx = contrast[["cells_group2_idx"]]

  # Create object
  if (contrast[["use_reduction"]]) {
    # Use dimensionality reduction
    contrast[["object"]] = Seurat::Reductions(sc, slot=contrast[["assay"]])
  } else {
    # Use assay
    contrast[["object"]] = Seurat::GetAssay(sc[,unique(c(cells_group1_idx, cells_group2_idx))], assay=contrast[["assay"]])
    
    # This saves a lot of memory for parallelisation
    if (contrast[["slot"]]!="scale.data") contrast[["object"]]@scale.data = new(Class="matrix")
  }
  
  # Variable latent vars must be passed as data.frame
  if (!is.null(contrast[["latent_vars"]]) && length(contrast[["latent_vars"]]) > 0) {
    contrast[["latent_vars"]] = sc[[unique(c(cells_group1_idx, cells_group2_idx)), contrast[["latent_vars"]], drop=FALSE]]
  }

  # Now update cell indices so that they match to subset
  contrast[["cells_group1_idx_subset"]] = match(colnames(sc)[cells_group1_idx], colnames(contrast[["object"]]))
  contrast[["cells_group2_idx_subset"]] = match(colnames(sc)[cells_group2_idx], colnames(contrast[["object"]]))
  
  return(contrast)
})

# Compute the tests
# TODO: this chunk may be done in parallel in future
degs_contrasts_results = purrr::map(degs_contrasts_list, function(contrast) {
  if (length(contrast$error_messages)==0) {
    # No errors, do contrast
    test_results = suppressWarnings(
      DegsTestCellSets(object=contrast[["object"]],
                       slot=contrast[["slot"]],
                       cells_1=colnames(contrast[["object"]])[contrast[["cells_group1_idx_subset"]]],
                       cells_2=colnames(contrast[["object"]])[contrast[["cells_group2_idx_subset"]]],
                       is_reduction=contrast[["use_reduction"]],
                       logfc.threshold=contrast[["log2FC"]],
                       test.use=contrast[["test"]],
                       min.pct=contrast[["min_pct"]],
                       latent.vars=contrast[["latent_vars"]])
    )
  } else {
    # Errors, return empty data.frame
    test_results = DegsEmptyResultsTable()
  }
  
  # Sort and filter table
  test_results = test_results %>% DegsSort() %>% DegsFilter(contrast[["log2FC"]], contrast[["padj"]], split_by_dir=FALSE)

  # Add mean gene expression data (counts or data, dep on slot)
  avg.1 = DegsAvgData(contrast[["object"]], cells=contrast[["cells_group1_idx_subset"]], genes=test_results$gene, slot=contrast[["slot"]])[,1]
  avg.2 = DegsAvgData(contrast[["object"]], cells=contrast[["cells_group2_idx_subset"]], genes=test_results$gene, slot=contrast[["slot"]])[,1]
  test_results = cbind(test_results, avg.1, avg.2)
  
  # Add test results and drop unneccessary data
  contrast = c(contrast, list(results=test_results))
  contrast[["object"]] = NULL
  contrast[["cells_group1_idx_subset"]] = NULL
  contrast[["cells_group2_idx_subset"]] = NULL
  
  return(contrast)
})

# Also remove objects from deg_contrasts_list (save memory)
degs_contrasts_list = purrr::map(degs_contrasts_list, function(contrast){ contrast[["object"]] = NULL; return(contrast)})

# Use the existing variable and add Enrichr results
# Not in parallel due to server load
degs_contrasts_results = purrr::map(degs_contrasts_results, function(contrast) {
  # Get results table
  results_table = contrast$results
  
  # Drop existing results
  if ("enrichr" %in% names(contrast)) contrast[["enrichr"]] = NULL
  
  # Split into up- and downregulated DEGs, then translate to Entrez gene, runEnrichr
  degs_up = dplyr::filter(results_table, avg_log2FC > 0) %>% dplyr::pull(gene) %>% unique()
  degs_up = sapply(degs_up, function(n) seurat_rowname_to_entrez[[n]][1], USE.NAMES=TRUE, simplify=TRUE) %>% unlist() %>% as.character()
  degs_up = degs_up[!is.na(degs_up)]
  enrichr_results_up = EnrichrTest(genes=degs_up, databases=param$enrichr_dbs, padj=param$enrichr_padj)
  
  degs_down = dplyr::filter(results_table, avg_log2FC < 0) %>% dplyr::pull(gene) %>% unique()
  degs_down = sapply(degs_down, function(n) seurat_rowname_to_entrez[[n]][1], USE.NAMES=TRUE, simplify=TRUE) %>% unlist() %>% as.character()
  degs_down = degs_down[!is.na(degs_down)]
  enrichr_results_down = EnrichrTest(genes=degs_down, databases=param$enrichr_dbs, padj=param$enrichr_padj)
  
  # Flatten both enrichr results into tables
  enrichr_results_up = purrr::map_dfr(names(enrichr_results_up), function(n) {
    return(cbind(enrichr_results_up[[n]], 
          list(Database=factor(rep(n, nrow(enrichr_results_up[[n]])), levels=names(enrichr_results_up)), 
               Direction=factor(rep("up", nrow(enrichr_results_up[[n]])), levels=c("up", "down"))
               )
          ))
  })
  
  enrichr_results_down = purrr::map_dfr(names(enrichr_results_down), function(n) {
    return(cbind(enrichr_results_down[[n]], 
          list(Database=factor(rep(n, nrow(enrichr_results_down[[n]])), levels=names(enrichr_results_down)), 
               Direction=factor(rep("up", nrow(enrichr_results_down[[n]])), levels=c("up", "down"))
               )
          ))
  })
  
  # Rbind and add factor levels
  enrichr_results = rbind(enrichr_results_up, enrichr_results_down)
  return(c(contrast, list(enrichr=enrichr_results)))
})

# Now regroup list so that subsets are together again
original_contrast_rows = purrr::map_int(degs_contrasts_results, function(contrast){ return(contrast[["contrast_row"]]) })
degs = split(degs_contrasts_results, original_contrast_rows)

# Write degs to files
invisible(purrr::map_chr(degs, function(degs_subsets) {
  # The output file
  file = file.path(param$path_out, "marker_degs", paste0("degs_contrast_row_", degs_subsets[[1]][["contrast_row"]], "_results.xlsx"))
  
  # Write degs
  degs_subsets_results = purrr::map(degs_subsets, function(contrast) {return(contrast[["results"]])})
  names(degs_subsets_results) = purrr::map_chr(degs_subsets, function(contrast) {return(ifelse(!is.na(contrast[["subset_group"]]), contrast[["subset_group"]], "All"))})
  file = DegsWriteToFile(degs_subsets_results, 
                         annot_ensembl=annot_ensembl,
                         gene_to_ensembl=seurat_rowname_to_ensembl,
                         file=file,
                         additional_readme=NULL)
  
  return(file)
}))


invisible(purrr::map_chr(degs, function(degs_subsets) {
  # The output file
  file = file.path(param$path_out, "marker_degs", paste0("degs_contrast_row_", degs_subsets[[1]][["contrast_row"]],  "_functions.xlsx"))
  
  # Write Enrichr results
  degs_subsets_enrichr = purrr::map(degs_subsets, function(contrast) {return(contrast[["enrichr"]])})
  names(degs_subsets_enrichr) = purrr::map_chr(degs_subsets, function(contrast) {return(ifelse(!is.na(contrast[["subset_group"]]), contrast[["subset_group"]], "All"))})
  file = EnrichrWriteResults(degs_subsets_enrichr, file=file)
  
  return(file)
}))

# Add to sc object
Misc(sc, "degs") = degs

for (i in seq(degs)) { 
  for (j in seq(degs[[i]])) {

    # Average expression of all genes
    x = subset(sc, cells=degs[[i]][[j]]$cells_group2_idx) %>% GetAssayData(slot="data")
    x = log2(Matrix::rowMeans(expm1(x)) + 1)
    y = subset(sc, cells=degs[[i]][[j]]$cells_group1_idx) %>% GetAssayData(slot="data") 
    y = log2(Matrix::rowMeans(expm1(y)) + 1)
    sc_avg_log2FC = data.frame(x, y, col="none", gene=rownames(sc))
    lims = c(min(c(x, y)), max(x, y))
    
    ## Color DEGs
    up = degs[[i]][[j]]$results %>% dplyr::filter(avg_log2FC > 0) %>% dplyr::pull(gene)
    if (length(up) > 0) sc_avg_log2FC[up, "col"] = "up"
    down = degs[[i]][[j]]$results %>% dplyr::filter(avg_log2FC < 0) %>% dplyr::pull(gene)
    if (length(down) > 0) sc_avg_log2FC[down, "col"] = "down"

    ## Plots
    degs[[i]][[j]]$plot_scatter = ggplot(sc_avg_log2FC %>% dplyr::arrange(col, gene), aes(x=x, y=y, col=col)) + 
      geom_abline(slope=1, intercept=0, col="lightgrey") + 
      geom_abline(slope=1, intercept=c(-degs[[i]][[j]]$log2FC, degs[[i]][[j]]$log2FC), col="lightgrey", lty=2) + 
      geom_point() + 
      xlim(lims) + ylim(lims) +
      AddStyle(ylab=degs[[i]][[j]]$condition_group1, xlab=degs[[i]][[j]]$condition_group2, 
               col=c(none="grey", up="darkgoldenrod1", down="steelblue"), 
               legend_position="bottom", legend_title="Filtered genes")

    # Feature plot of top 4 genes, sorted by the p-value
    degs_top = degs[[i]][[j]]$results %>% dplyr::top_n(n=-4, wt=p_val) %>% dplyr::top_n(n=-4, wt=avg_log2FC) %>% dplyr::pull(gene)
    if (length(degs_top) > 0) {
      p_list = Seurat::FeaturePlot(sc, features=degs_top, cols=c("lightgrey", param$col), combine=FALSE, label=TRUE)
      for (p in seq(p_list)) p_list[[p]] = p_list[[p]] + AddStyle(legend_position="bottom", xlab="", ylab="")
      degs[[i]][[j]]$plot_feature = patchwork::wrap_plots(p_list, ncol=ifelse(length(degs_top) > 1, 2, 1))
    } else {
      degs[[i]][[j]]$plot_feature = NULL
    }
  }
}

knitr_header_string = "

## {{condition_column}}: {{condition_group1}} vs {{condition_group2}}

General configuration:

* assay: {{assay}}
* slot: {{slot}}
* test: {{test}}
* maximum adjusted p-value: {{padj}}
* minimum absolute log2 foldchange: {{log2FC}}
* minimum percentage of cells: {{min_pct}}
* latent vars: {{latent_vars}}

Subset on column: \'{{subset_column}}\'"

if (length(degs)==0) message("No DEG contrasts specified.")

for (i in seq(degs)) {
  # Get subsets
  degs_subsets = degs[[i]]
  first_contrast = degs_subsets[[1]]
  
  # Create header
  cat(
    knitr::knit_expand(text=knitr_header_string,
                     condition_column=first_contrast[["condition_column"]],
                     condition_group1=first_contrast[["condition_group1"]],
                     condition_group2=first_contrast[["condition_group2"]],
                     assay=first_contrast[["assay"]],
                     slot=first_contrast[["slot"]],
                     test=first_contrast[["test"]],
                     padj=first_contrast[["padj"]],
                     log2FC=first_contrast[["log2FC"]],
                     min_pct=first_contrast[["min_pct"]],
                     latent_vars=ifelse(!is.null(first_contrast[["latent_vars"]]), paste(colnames(first_contrast[["latent_vars"]]), collapse=","), "-"),
                     subset_column=ifelse(is.na(first_contrast[["subset_column"]]), "-", first_contrast[["subset_column"]]))
  , "\n")
  
  # Get error messages
  error_messages = unique(purrr::flatten_chr(purrr::map(degs_subsets, function(contrast){return(contrast[["error_messages"]])})))

   # Create combined results table
  degs_subsets_results = purrr::map_dfr(degs_subsets, function(contrast) {
    subset_group_value = ifelse(!is.na(contrast[["subset_group"]]), contrast[["subset_group"]], "All")
    return(contrast[["results"]] %>% 
      dplyr::summarise(subset_group=subset_group_value,
                       Cells1=length(contrast[["cells_group1_idx"]]),
                       Cells2=length(contrast[["cells_group2_idx"]]),
                       DEGs=length(gene),
                       DEGs_up=sum(avg_log2FC > 0),
                       DEGs_down=sum(avg_log2FC < 0)))
  }) %>% tibble::as_tibble()
  
  # Print warnings/errors
  if (length(error_messages) > 0) {
    warning(error_messages)
  }
  
  # Print summary table
  print(
      knitr::kable(degs_subsets_results,
                   align="l", caption="DEG summary", 
                   col.names=c("Subset", "Cells in group 1", "Cells in group 2", "# DEGs", "# DEGs upregulated", "# DEGs downregulated"), 
                   format="html") %>%
        kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))
    )
  
  # Print plots per contrast
  for (contrast in seq(degs_subsets)) {
    p = degs_subsets[[contrast]]$plot_scatter | degs_subsets[[contrast]]$plot_feature
    title = "Scatterplot and feature plots"
    if (!is.na(degs_subsets[[contrast]]$subset_column)) {
      title = paste0(title, " (subset ", degs_subsets[[contrast]]$subset_column, 
                     ": ", degs_subsets[[contrast]]$subset_group, ")")
    }
    
    p = p + patchwork::plot_annotation(title=title)
    print(p)
  }
  
  cat("\n \n")
} 

orig.ident: pbmc_10x vs pbmc_smartseq2_sample1

General configuration:

• assay: RNA
• slot: data
• test: wilcox
• maximum adjusted p-value: 0.05
• minimum absolute log2 foldchange: 0
• minimum percentage of cells: 0.1
• latent vars: -

Subset on column: ‘-’

DEG summary

Subset	Cells in group 1	Cells in group 2	# DEGs	# DEGs upregulated	# DEGs downregulated
All	100	100	630	232	398

orig.ident: pbmc_10x vs pbmc_smartseq2_sample1

General configuration:

• assay: RNA
• slot: data
• test: wilcox
• maximum adjusted p-value: 0.05
• minimum absolute log2 foldchange: 0
• minimum percentage of cells: 0.1
• latent vars: -

Subset on column: ‘seurat_clusters’

DEG summary

Subset	Cells in group 1	Cells in group 2	# DEGs	# DEGs upregulated	# DEGs downregulated
1	50	47	241	100	141
2	35	37	229	107	122
3	11	16	2	2	0

Phase: G1 vs G2M

General configuration:

• assay: RNA
• slot: data
• test: wilcox
• maximum adjusted p-value: 0.05
• minimum absolute log2 foldchange: 0
• minimum percentage of cells: 0.1
• latent vars: -

Subset on column: ‘seurat_clusters’

DEG summary

Subset	Cells in group 1	Cells in group 2	# DEGs	# DEGs upregulated	# DEGs downregulated
1	30	30	0	0	0
2	17	24	0	0	0

Output

# Add colour lists for orig.dataset
col = GenerateColours(num_colours=length(levels(sc$orig.dataset)), names=levels(sc$orig.dataset), palette=param$col_palette_samples, alphas=1)
sc = ScAddLists(sc, lists=list(orig.dataset=col), lists_slot="colour_lists")

# Add experiment details
Seurat::Misc(sc, "experiment") = list(project_id=param$project_id, date=Sys.Date(), species=gsub("_gene_ensembl", "", param$mart_dataset))

# Add parameter
Seurat::Misc(sc, "parameters") = param

# Add technical output
session_info = suppressMessages(sessioninfo::session_info())
Seurat::Misc(sc, "technical") = list(scrnaseq_git=GitRepositoryVersion(param$path_to_git),
                                     R=session_info$platform$version,
                                     packages=as.data.frame(session_info$packages)[, c("package", "ondiskversion", "loadedversion", "date")])

Export to Loupe Cell Browser

If all provided datasets are of type “10x,” we export the UMAP 2D visualisation, metadata such as the cell clusters, and lists of differentially expressed genes, so you can open and work with these in the Loupe Cell Browser https://support.10xgenomics.com/single-cell-gene-expression/software/visualization/latest/what-is-loupe-cell-browser.

if (all(param$path_data$type == "10x")) { 
  
  # Export reductions (umap, pca, others)
  for(r in Seurat::Reductions(sc)) {
    write.table(Seurat::Embeddings(sc, reduction=r)[,1:2] %>% as.data.frame() %>% tibble::rownames_to_column(var="Barcode"),
                file=file.path(param$path_out, "export", paste0("Loupe_projection_", r, ".csv")), col.names=TRUE, row.names=FALSE, quote=FALSE, sep=",")
  }
  
  # Export categorical metadata
  loupe_meta = sc@meta.data
  idx_keep = sapply(1:ncol(loupe_meta), function(x) !is.numeric(loupe_meta[,x]))
  write.table(x=loupe_meta[, idx_keep] %>% tibble::rownames_to_column(var="barcode"), 
              file=file.path(param$path_out, "export", "Loupe_metadata.csv"), col.names=TRUE, row.names=FALSE, quote=FALSE, sep=",")

  # Export gene sets (CC genes, known markers, per-cluster markers up- and downregulated, ...)
  gene_lists = Misc(sc, "gene_lists")
  
  # Remove empty gene sets
  gene_lists = gene_lists[purrr::map_int(gene_lists, length) > 0]
  loupe_genesets = purrr::map_dfr(names(gene_lists), function(n) {return(data.frame(List=n, Name=gene_lists[[n]]))})
  loupe_genesets$Ensembl = seurat_rowname_to_ensembl[loupe_genesets$Name]
  write.table(loupe_genesets, file=file.path(param$path_out, "export", "Loupe_genesets.csv"), col.names=TRUE, row.names=FALSE, quote=FALSE, sep=",")
}

Result files are:

• export/
  • Loupe_projection_(umap|pca|…).csv: Seurat UMAP/PCA/… projections for visualization
  • Loupe_metadata.csv: Seurat cell meta data including clusters and cell cycle phases
  • Loupe_genesets.csv: Gene sets such as markers, DEGs, cell cycles

Projections can be imported in Loupe via “Import Projection,” cell meta data via “Import Categories” and gene sets via “Import Lists”

Export to cellxgene browser

We export the assay data, cell metadata, clustering and visualisation in a format that can be read by the cellxgene browser https://github.com/chanzuckerberg/cellxgene.

# Convert Seurat single cell object to python anndata object which will be accessible via reticulate here
adata = sceasy::convertFormat(sc, from="seurat", to="anndata", outFile=NULL, assay=DefaultAssay(sc))

# Set up correct colours (see https://chanzuckerberg.github.io/cellxgene/posts/prepare) so that colours match
adata$uns = dict(
  seurat_clusters_colors=np_array(unname(Misc(sc, "colour_lists")$seurat_clusters), dtype="<U7"), 
  orig.ident_colors=np_array(unname(Misc(sc, "colour_lists")$orig.ident), dtype="<U7"),
  orig.dataset_colors=np_array(unname(Misc(sc, "colour_lists")$orig.dataset), dtype="<U7"),
  Phase_colors=np_array(unname(Misc(sc, "colour_lists")$Phase), dtype="<U7")
)

# Write to h5ad file
adata$write(file.path(param$path_out, "export", "sc.h5ad"), compression="gzip")

Result files are:

• export/
  • sc.h5ad: H5AD object for cellxgene browser

Copy/upload to data directory of your cellxgene browser

Export to the Cerebro browser

We export the assay data, clustering, visualisation, marker genes, enriched pathways and degs in a format that can be read by the Cerebro Browser https://github.com/romanhaa/cerebroApp/.

# Export to cerebro
res = ExportToCerebro(sc=sc, 
                      path=file.path(param$path_out, "export", "sc.crb"), 
                      assay=DefaultAssay(sc),
                      assay_raw=param$assay_raw,
                      delayed_array=FALSE)

Result files are:

• export/
  • sc.crb: Object for Cerebro browser

Load into Cerebro browser.

Other output files

# Seurat object
saveRDS(sc, file=file.path(param$path_out, "data", "sc.rds"))

# Counts (RNA)
invisible(ExportSeuratAssayData(sc, 
                      dir=file.path(param$path_out, "data", "counts"), 
                      assays=param$assay_raw, 
                      slot="counts",
                      include_cell_metadata_cols=colnames(sc[[]]),
                      metadata_prefix=paste0(param$project_id, ".")))

# Metadata
openxlsx::write.xlsx(x=sc[[]] %>% tibble::rownames_to_column(var="Barcode"), file=file.path(param$path_out, "data", "cell_metadata.xlsx"), rowNames=FALSE, colNames=TRUE)

# Annotation as excel file
openxlsx::write.xlsx(x=data.frame(seurat_id=rownames(sc), ensembl_gene_id=seurat_rowname_to_ensembl[rownames(sc)]) %>%
                       dplyr::inner_join(annot_ensembl, by="ensembl_gene_id"),
                     file=file.path(param$path_out, "annotation", "gene_annotation.xlsx"), 
                     rowNames=FALSE, colNames=TRUE)

# Data and annotation for a subset of 500 cells for Morpheus
# ERROR: This gives an error with the newest packages, might be related to this: 
# https://issueexplorer.com/issue/r-lib/cpp11/244
#  For now, we outcomment this section, since it isn't really needed
#cells_subset = ScSampleCells(sc, n=500, seed=1)
#openxlsx::write.xlsx(Seurat::GetAssayData(sc[, cells_subset], slot="data") %>% as.data.frame() %>% tibble::rownames_to_column(var="gene"), 
#                     file=file.path(param$path_out, "data", paste0("subset_", 500, "_cells_normalised_data.xlsx")),
#                     rowNames=FALSE, colNames=TRUE)
#openxlsx::write.xlsx(Seurat::GetAssayData(sc[, cells_subset], slot="scale.data") %>% as.data.frame() %>% #tibble::rownames_to_column(var="gene"), 
#                     file=file.path(param$path_out, "data", paste0("subset_", 500, "_cells_scaled_data.xlsx")),
#                     rowNames=FALSE, colNames=TRUE)
#openxlsx::write.xlsx(sc[[]][cells_subset, ] %>% as.data.frame() %>% tibble::rownames_to_column(var="cell"), 
#           file=file.path(param$path_out, "data",paste0("subset_", 500, "_cells_column_annotations.xlsx")),
#           rowNames=FALSE, colNames=TRUE)

Result files are:

• annotation/
  • gene_annotation.xlsx: Ensembl annotation of the genes in Excel format
  • cell_cycle_markers.xlsx: Markers for cell cycle phases in Excel format
  • species_gene_ensembl.vEnsembl.annot.txt: Raw annotation downloaded from Ensembl as tab-separated file
• data/
  • sc.rds: Seurat object for import into R
  • cell_metadata.xlsx: Cell metadata in Excel format
  • counts: Raw counts of the entire dataset as sparse matrix
  • subset_500_cells_normalised_data.xlsx: Normalised subset of 500 cells in Excel format (for heatmap plotting with morpheus (https://software.broadinstitute.org/morpheus/))
  • subset_500_cells_scaled_data.xlsx: Normalised and scaled subset of 500 cells in Excel format (for heatmap plotting with morpheus)
  • subset_500_cells_column_annotations.xlsx: Cell annotations for the 500 cells subset in Excel format (for heatmap plotting with morpheus)
• figures/
  • all plots of the HTML report in PNG and PDF format
• marker_degs/
  • markers_cluster_vs_rest.xslx: Marker genes for each cluster in Excel format
  • functions_marker_up_cluster_[1,2,3,…]_vs_rest.xslx: Biological functions and pathways that were found enriched in the up-regulated markers for cluster 1,2,3,… in Excel format
  • functions_marker_down_cluster_[1,2,3,…]_vs_rest.xslx: Biological functions and pathways that were found enriched in the down-regulated markers for cluster 1,2,3,… in Excel format
  • degs_contrast_row_[1,2,3,…]_results.xlsx: Results (genes) of the DEG analysis specified in the first,second,third,… row of the configuration parameter (Excel format)
  • degs_contrast_row_[1,2,3,…]_functions.xlsx: Results (enriched functions and pathways) of the DEG analysis specified in the first,second,third,… row of the configuration parameter (Excel format)

Parameter table

The following parameters were used to run the workflow.

out = ScrnaseqParamsInfo(params=param)

knitr::kable(out, align="l") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"), full_width=FALSE, position="left")

Name	Value
author	kasa397b
project_id	pbmc
path_data	name:pbmc_10x, pbmc_smartseq2; type:10x, smartseq2; path:test_datasets/10x_SmartSeq2_pbmc_GSE132044/counts/10x/, test_datasets/10x_SmartSeq2_pbmc_GSE132044/counts/smartseq2/counts_table.tsv.gz; stats:NA, NA; suffix:-1, -2
assay_raw	RNA
downsample_cells_n	100
path_out	test_datasets/10x_SmartSeq2_pbmc_GSE132044/results/
file_known_markers	test_datasets/10x_SmartSeq2_pbmc_GSE132044/known_markers.xlsx
mart_dataset	hsapiens_gene_ensembl
annot_version	98
annot_main	ensembl=ensembl_gene_id, symbol=external_gene_name, entrez=entrezgene_accession
mart_attributes	ensembl_gene_id, external_gene_name, entrezgene_accession, chromosome_name, start_position, end_position, percentage_gene_gc_content, gene_biotype, strand, description
biomart_mirror	www
mt	^MT-
cell_filter	pbmc_10x:nFeature_RNA=c(NA, NA), percent_mt=c(NA, NA); pbmc_smartseq2_sample1:nFeature_RNA=c(NA, NA), percent_mt=c(NA, NA)
feature_filter	pbmc_10x:min_counts=1, min_cells=3; pbmc_smartseq2_sample1:min_counts=1, min_cells=3
samples_min_cells	10
norm	RNA
cc_remove	FALSE
cc_remove_all	FALSE
cc_rescore_after_merge	TRUE
integrate_samples	method:integrate; dimensions:30; reference:; use_reciprocal_pca:FALSE
pc_n	10
cluster_resolution_test	0.3, 0.7
cluster_resolution	0.5
marker_padj	0.05
marker_log2FC	1
marker_pct	0.25
deg_contrasts	condition_column:orig.ident, orig.ident, Phase; condition_group1:pbmc_10x, pbmc_10x, G1; condition_group2:pbmc_smartseq2_sample1, pbmc_smartseq2_sample1, G2M; subset_column:NA, seurat_clusters, seurat_clusters; subset_group:NA, , 1;2; downsample_cells_n:NA, 50, 30
enrichr_site	Enrichr
enrichr_padj	0.05
enrichr_dbs	GO_Molecular_Function_2018, GO_Biological_Process_2018, GO_Cellular_Component_2018, Azimuth_Cell_Types_2021, CellMarker_Augmented_2021, Descartes_Cell_Types_and_Tissue_2021
col	palevioletred
col_palette_samples	ggsci::pal_jama
col_palette_clusters	ggsci::pal_igv
path_to_git	.
debugging_mode	default_debugging
file_annot	test_datasets/10x_SmartSeq2_pbmc_GSE132044/results//annotation/hsapiens_gene_ensembl.v98.annot.txt
file_cc_genes	test_datasets/10x_SmartSeq2_pbmc_GSE132044/results//annotation/cell_cycle_markers.xlsx
samples_to_drop
col_samples	pbmc_10x=#374E55FF, pbmc_smartseq2_sample1=#DF8F44FF
col_clusters	1=#5050FFFF, 2=#CE3D32FF, 3=#749B58FF

Software versions

This report was created with generated using the scrnaseq GitHub repository. Software versions were collected at run time.

out = ScrnaseqSessionInfo(param$path_to_git)

knitr::kable(out, align="l") %>% 
  kableExtra::kable_styling(bootstrap_options=c("striped", "hover"))

Name	Version
ktrns/scrnaseq	7236e8274248ebe24e15ac89cfd13b7a51319288
Container	NA
R	R version 4.0.3 (2020-10-10)
Platform	x86_64-conda-linux-gnu (64-bit)
Operating system	Debian GNU/Linux 10 (buster)
Packages	abind1.4-5, annotate1.68.0, AnnotationDbi1.52.0, ape5.5, askpass1.1, assertthat0.2.1, babelgene21.4, bibtex0.4.2.3, Biobase2.50.0, BiocFileCache1.14.0, BiocGenerics0.36.0, BiocParallel1.24.1, biomaRt2.46.3, bit4.0.4, bit644.0.5, bitops1.0-7, blob1.2.1, bslib0.2.4, cachem1.0.4, cerebroApp1.3.1, cli3.1.0, cluster2.1.1, codetools0.2-18, colorspace2.0-2, colourpicker1.1.1, cowplot1.1.1, crayon1.4.2, curl4.3.2, data.table1.14.0, DBI1.1.1, dbplyr2.1.1, DelayedArray0.16.3, deldir1.0-6, digest0.6.28, dplyr1.0.5, DT0.17, ellipsis0.3.2, enrichR3.0, evaluate0.14, fansi0.5.0, farver2.1.0, fastmap1.1.0, fitdistrplus1.1-6, fs1.5.0, future1.23.0, future.apply1.8.1, generics0.1.1, GenomeInfoDb1.26.4, GenomeInfoDbData1.2.4, GenomicRanges1.42.0, ggplot23.3.5, ggrepel0.9.1, ggridges0.5.3, ggsci2.9, globals0.14.0, glue1.5.0, goftest1.2-3, graph1.68.0, gridExtra2.3, GSEABase1.52.1, GSVA1.38.2, gtable0.3.0, highr0.8, hms1.0.0, htmltools0.5.1.1, htmlwidgets1.5.3, httpuv1.5.5, httr1.4.2, ica1.0-2, igraph1.2.6, IRanges2.24.1, irlba2.3.3, jquerylib0.1.3, jsonlite1.7.2, kableExtra1.3.4, KernSmooth2.23-18, knitcitations1.0.12, knitr1.31, labeling0.4.2, later1.1.0.1, lattice0.20-41, lazyeval0.2.2, leiden0.3.9, lifecycle1.0.1, limma3.46.0, listenv0.8.0, lmtest0.9-38, lubridate1.8.0, magrittr2.0.1, MASS7.3-53.1, MAST1.16.0, Matrix1.3-4, MatrixGenerics1.2.1, matrixStats0.58.0, memoise2.0.0, mgcv1.8-34, mime0.12, miniUI0.1.1.1, msigdbr7.4.1, munsell0.5.0, nlme3.1-152, openssl1.4.5, openxlsx4.2.3, parallelly1.28.1, patchwork1.1.1, pbapply1.5-0, pillar1.6.4, pkgconfig2.0.3, plotly4.10.0, plyr1.8.6, png0.1-7, polyclip1.10-0, prettyunits1.1.1, progress1.2.2, promises1.2.0.1, purrr0.3.4, qvalue2.22.0, R.methodsS31.8.1, R.oo1.24.0, R.utils2.10.1, R62.5.1, RANN2.6.1, rappdirs0.3.3, RColorBrewer1.1-2, Rcpp1.0.7, RcppAnnoy0.0.19, RCurl1.98-1.3, readr1.4.0, RefManageR1.3.4, reshape21.4.4, reticulate1.18, rjson0.2.20, rlang0.4.12, rmarkdown2.7, ROCR1.0-11, rpart4.1-15, RSpectra0.16-0, RSQLite2.2.8, rstudioapi0.13, Rtsne0.15, rvest1.0.0, S4Vectors0.28.1, sass0.3.1, scales1.1.1, scattermore0.7, sceasy0.0.6, sctransform0.3.2, sessioninfo1.1.1, Seurat4.0.5, SeuratObject4.0.3, shiny1.6.0, shinycssloaders1.0.0, shinydashboard0.7.2, shinyFiles0.9.1, shinyjs2.0.0, shinyWidgets0.6.2, SingleCellExperiment1.12.0, spatstat.core2.3-1, spatstat.data2.1-0, spatstat.geom2.3-0, spatstat.sparse2.0-0, spatstat.utils2.2-0, stringi1.7.5, stringr1.4.0, SummarizedExperiment1.20.0, survival3.2-10, svglite2.0.0, systemfonts1.0.3, tensor1.5, tibble3.1.6, tidyr1.1.3, tidyselect1.1.0, utf81.2.2, uwot0.1.10, vctrs0.3.8, viridis0.6.2, viridisLite0.4.0, webshot0.5.2, withr2.4.2, xfun0.20, XML3.99-0.6, xml21.3.2, xtable1.8-4, XVector0.30.0, yaml2.2.1, zip2.1.1, zlibbioc1.36.0, zoo1.8-9

Credits and References

This Workflow was developed as part of the scrnaseq GitHub repository by Katrin Sameith and Andreas Petzold at the Dresden-concept Genome Center, TU Dresden (Dresden, Germany), in collaboration with Torsten Glomb, Maike Kosanke and Oliver Dittrich at the Research Core Unit Genomics, Hannover Medical School (Hannover, Germany). The Seurat Vignettes were initially used as templates.

# Writes knitcitations references to references.bib file.
knitcitations::write.bibtex(file=file.path(param$path_out, "references.bib"))